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Answer the following questions using the material from this module. Your post should be 500 words (5 points) and free of grammatical and spelling mistakes (5 points).

1. Why do you think criminology has focused on males? What effect does this have on our knowledge of crime and delinquency? (10 points)

2. How does disproportionate minority contact affect the community? What can be reasonably done to remedy this? (10 points)

3. What did the Johnson article find in regards to long-term effects of the justice system? (10 points)

4. Ask a question to your classmates. This can be a question meant to garner better understanding of the material or just a question about the material. (5 points)

5. Respond to another student by answering the question they posed in part 4 above with a minimum of 150 words. (5 points) \ for this question, I will send the question from one of the classmates for you after you complete the first four questions because it will be visible after I post the answers for the first four questions.

J Child Fam Stud (2015) 24:427–433
DOI 10.1007/s10826-013-9853-8
ORIGINAL PAPER
Females in the Juvenile Justice System: Influences on Delinquency
and Recidivism
David E. Barrett • Song Ju • Antonis Katsiyannis
Dalun Zhang
•
Published online: 19 October 2013
Ó Springer Science+Business Media New York 2013
Abstract The role of mental health history and family
dysfunction or disruption on female juvenile delinquency
was examined. Using large sample archival data from a
state juvenile justice agency, we examined the behavioral
and demographic predictors of repeat offending for a
sample of approximately 34,000 females who had been
referred for criminal offenses. Then, after merging these
data with those from multiple state agencies, we compared
the family and mental health histories of the delinquent
females with those of females from a matched control
group of the same number, constructed from the records of
the state department of education. Drug use, family delinquency, severity of first offenses, and age of first offending
were predictors of repeat offending for the females in the
delinquent sample. Compared with non-delinquent
females, delinquent females were more likely to be eligible
for free or reduced lunch, and were more likely to have
been in foster care or child protective services. The
strongest predictor of membership in the delinquent sample
was a DSM-IV diagnosis of a mental health disorder
related to aggression or impulse control. All variables
associated with delinquency remained significant when
other predictors were statistically controlled. Implications
for prevention of female juvenile delinquency were
addressed.
D. E. Barrett (&) A. Katsiyannis
Department of Teacher Education, Clemson University,
Clemson, SC 29634, USA
e-mail: bdavid@clemson.edu
S. Ju
University of Cincinnati, Cincinnati, OH, USA
D. Zhang
Texas A&M University, College Station, TX, USA
Keywords Female delinquency Child
maltreatment Mental health and delinquency Child
protective services Juvenile justice
Introduction
In 2009, 1,906,600 juveniles were arrested in the United
States for violent crimes (85,890 murder and non-negligent
manslaughter, forcible rape, robbery, and aggravated
assault), property crimes (417,700 burglary, larceny-theft,
motor vehicle theft, and arson), and non-index crimes
(1,403,010 crimes such as other assaults, drug abuse, disorderly conduct, violation of liquor laws, status offenses).
Regarding violent crimes in 2009, 47 % involved white
youth, 51 % black youth, 1 % Asian youth, and 1 %
American Indian youth. For property crime arrests, 64 %
involved white youth, 33 % black youth, 2 % Asian youth,
and 1 % American Indian youth (Puzzanchera and Adams
2011).
In 2009, 578,500 females were arrested. These arrests
accounted for 18 % of juvenile Violent Crime Index arrests
and 38 % of juvenile Property Crime Index arrests. Of nonindex crimes, arrest rates for females were 78 % for
prostitution, 55 % runaways, 42 % embezzlement, and
34 % other assaults. From 2000 through 2009, arrests of
juvenile females decreased less than male arrests in offense
categories such as aggravated assault, vandalism, and drug
abuse violations. Female arrests increased while male
arrests decreased for crimes such as simple assault, larceny-theft, and disorderly conduct. Further, whereas in
1980, the juvenile male violent crime arrest rate was 8
times greater than the female rate, in 2009 the male rate
was just 4 times greater (Puzzanchera and Adams 2011).
Overall females are more likely than males to be detained
for status offenses (truancy, running away, underage
123
428
drinking) while males are more likely to commit more
serious offenses (Boesky 2002; Puzzanchera and Adams
2011).
Female delinquency follows specific patterns relating to
age and context. Early puberty when paired with family
conflict and neighborhoods characterized by poverty,
unemployment, and single parent families is a unique risk
factor for females (Zahn et al. 2010). Disorganized communities tend to exacerbate the frequency of violent acts
(Burman 2003), with gang membership associated with
more violent behavior among females (Zahn et al. 2008a).
There is some evidence that aggressive behavior among
female youth is associated with girls’ ambivalence
regarding obedience to parental authority. For example,
girls are more likely to fight with family members (Franke
et al. 2002) than with non-family members. Data on arrests
for intra-family aggressive behavior must be interpreted
cautiously, however; domestic behaviors which under
certain conditions might be considered ‘‘ungovernable’’
(and result in a referral for a status offense) might, in a
domestic situation, result instead in an arrest for simple
assault (Chesney-Lind and Sheldon 2004; Gaarder et al.
2004; Zahn et al. 2008a).
Despite the increasing rates of female juvenile delinquency, particularly for crimes traditionally associated with
males, addressing the needs of females in the juvenile
system has been a persistent challenge (Boesky 2002;
Quinn et al. 2005; Teplin et al. 2002). The American Bar
Association and National Bar Association Report (2001)
have concluded that there is a critical lack of prevention,
diversion, and treatment alternatives for girls in the juvenile justice system. For example, not only do females tend
to commit less serious crimes than males, they often
receive differential treatment for similar crimes (Barrett
et al. 2010; Miller et al. 1995). Consequently, many professionals argue that the majority of female delinquency
cases should be diverted from court proceedings (Bishop
and Frazier 1992; Quinn et al. 2005). Females are also
prone to emotional, physical, or sexual abuse (Acoca and
Dedel 1998; Berlinger and Elliot 2002; Teplin et al. 2002).
In fact, physical assault by a parent or caregiver, sexual
assault or neglect by a parent or caregiver, and neighborhood disadvantage are key risk factors for delinquency
(Hawkins et al. 2009) along with family criminality, drug
use, and deviance (Zahn et al. 2010). In contrast, the presence of a caring adult, school success, and religiosity have
been shown to serve as protective factors (Hawkins et al.
2009).
In the present study we were able to obtain detailed
background information on the early experiences of over
34,000 females with records of juvenile delinquency. Using
information from a state department of juvenile justice, we
examined the role of selected family and demographic
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J Child Fam Stud (2015) 24:427–433
variables in predicting female recidivism. Using information from the state’s budget and control board, we then
constructed a control group of 34,000 female youth without
histories of delinquency and matched on birth year and
race. By linking records on delinquency with records from
other state agencies, we were able to examine the influences of important child personal and experiential variables
including mental health history, maltreatment and foster
care on juvenile delinquency. Our study addressed two
major research questions. First, among delinquent females,
what are the personal and family background variables
which are useful in predicting female recidivism? Second,
to what extent can we predict membership in the delinquent
group versus the non-delinquent control group on the basis
of females’ emotional/behavioral problems and early
adverse family experiences?
Method
Sample
Data from eight cohorts of female juvenile offenders were
drawn from the South Carolina Department of Juvenile
Justice (SCDJJ) Management Information System. The
sample included 34,414 female juvenile offenders born
between 1981 and 1988, each of whom had been referred to
SCDJJ on at least one occasion. The SCDJJ is a state
cabinet agency which covers 43 of 46 counties in South
Carolina. When a juvenile is arrested or referred by a
Circuit Solicitor or a school, a SCDJJ county office will
perform the family court intake and make a recommendation to the Solicitor’s Office with advisory recommendations (e.g., diversion or prosecution). The family court
intake involves collecting data from parents or guardians
on the child’s gender, ethnicity, and date of birth; documenting the nature of the referral offense; and performing
risk and needs assessments. Data collection may also
involve other social-demographic variables, including
family income, family history of delinquency, child substance use, educational history, and family structure (see
Barrett et al. 2010).
The sample for this analysis included 34,614 female
juveniles whose age at first referral ranged from 5 to 19
(M = 14.67, SD = 1.80). A small percentage (1.4 %) of
juveniles who were not African Americans or Caucasian
(n = 486) were excluded from this study. Demographic
information is presented in Table 1. As shown in Table 1,
the racial composition was 18,007 (51.3 %) African
American and 16,607 (47.3 %) Caucasian. With respect to
numbers of referrals, 13,300 (38.4 %) had one referral
only, 13,915 (40.2 %) had two referrals, and 7,399
(21.8 %) had three or more referrals. At the statewide level,
J Child Fam Stud (2015) 24:427–433
429
Table 1 Demographic characteristics of delinquent females
Characteristics
N
%
Black
18,007
52
White
16,607
48
Total N
34,614
Race
Family delinquency
Yes
6,001
53.4
No
5,232
46.6
Total N
11,233
Family income
\$15,000
7,751
48.9
C15,000
Total N
8,096
15,847
51.1
Yes
4,396
25.1
No
13,102
74.9
Total N
17,498
Drug use history
diagnosed as having disorder of aggression and/or impulse
control based on DSM-IV classification); see Barrett et al.
2013b for information regarding criteria for evaluating a
child as having an aggression-related disorder. Variables
created from the different agency files were merged to
create a new master dataset for the delinquent sample.
Finally, a comparison, non-delinquent sample was randomly selected from a previously created control sample of
99,602 youth using data made available from SCDE; the
non-delinquent sample was constructed to have the same
proportions of birth years, gender, and ethnicities as the
DJJ cohort. Additional information about the matching
procedure has been reported (Barrett et al. 2013b). The
same data collected from the ORS for the delinquent
sample were extracted for the control group. The delinquent and non-delinquent files were merged for the purpose
of statistical analyses.
Data Analysis
1st referral severity
Misdemeanor
8,348
86.3
Felony
1,324
13.7
Total N
9,672
SCDJJ assigns all offenses a severity rating on a scale of
1–25, with ratings less than 2 representing status offenses
(e.g., truancy, running away), 2–3 representing misdemeanor offenses (e.g., simple assault and battery, criminal
domestic violence), 5–8 representing nonviolent felonies
(e.g., grand larceny, carrying a weapon on school grounds),
and 8.5–25 representing violent felonies (e.g., assault and
battery of a high and aggravated nature, sexual assault,
armed robbery). For analysis purposes, we recoded severity
of offenses into two levels, status offense or misdemeanor
(SCDJJ ratings 1 through 3) and felony (SCDJJ ratings 5
through 25).
Next, data from the DJJ were merged with data from the
SC State Budget and Control Board’s Office of Research
and Statistics (ORS). ORS data were collected from three
different state agencies: the Department of Social Services
(SCDSS), the Department of Mental Health (SCDMH), and
the Department of Education (SCDE). Each child in the
DJJ file has been assigned a unique ID generated through a
linkage algorithm and matched ORS data were then linked
to each child (see Barrett et al. 2013b for details). In this
study, variables extracted from ORS files included: (1)
foster care placements (i.e., whether or not a child had ever
been placed in foster care); (2) maltreatment [i.e., whether
or not a child had ever been placed in the custody of child
protective services (CPS)]; (3) free lunch (i.e., whether a
child was eligible for free and/or reduced lunch); and (4)
aggressive behaviors (i.e., whether a child had been
The data analysis plan included two steps. First, ‘‘proportional hazards regression’’ (also termed Cox regression)
was used to examine the risk of recidivism for female
offenders. This analysis technique is a type of survival/
failure analysis (Singer and Willett 2003) and has been
used previously to examine the likelihood of and timing of
recidivism (Zhang et al. 2011). This type of survival analysis typically examines how the risk of an adverse outcome changes over time in relation to possible covariate
effects. In this study, we used this technique to predict
recidivism (repeated offense) by time and by associated
juvenile characteristics which have been identified from
previous literature, including race, presence/absence of
family history of delinquency, family income, presence/
absence of personal drug use history, offense severity at 1st
referral, and age at first referral (Zhang et al. 2011; Barrett
et al. 2010). Data were obtained from the original SCDJJ
files (see Barrett et al. 2010). The hazards analysis was
performed using the augmented Cox regression model of
Lunn and McNeil (1995). The variable ‘‘number of referrals’’ was dummy coded with ‘‘one referral’’ coded as ‘‘0’’
and ‘‘multiple referrals’’ coded as ‘‘1’’; covariates were
included in the model. Censoring is used to determine the
termination of the period of time. In this study, censoring
was created for age 21, as this ended time to juvenile
offense.
Second, using the merged delinquent and control files
and data obtained from the ORS files, we examined differences between the behavioral, academic, and mental
health characteristics of females in the delinquent sample
and those in the control sample. First, we used Chi square
and phi coefficient analyses to compare proportions of
each group (delinquent and non-delinquent) showing
123
430
J Child Fam Stud (2015) 24:427–433
presence or absence of each indicator. Next multivariate
logistic regression (Hosmer and Lemeshow 2000) was
chosen to examine the variables that predicted whether a
female would be in the delinquent or the non-delinquent
sample. This analysis allowed us to examine the unique
as well as the collective contributions of each risk variable. Examined covariates included free lunch, placement
in CPS, placement in foster care and disorders of
aggression. Race was also included in the model.
Adjusted odds ratios and effect sizes are reported for the
logistic model.
Results
Influences on Recidivism
The results from the Cox regression analysis are presented in Table 2. Controlling the risk factor of race, all
covariates with the exception of family income were
significant individual predictors of recidivism. Specifically, offenders who were younger at the first referral
had a higher risk of recidivism (b = -.194, p \ .001)
with the chance of recidivism expected to decrease by
18 % for each additional year if all other variables are
held constant. Offenders who had a drug use history
were almost twice as likely to have a second offense as
those not having drug use history (b = .666, p \ .001).
Compared with offenders who committed more severe
offenses, offenders who committed less severe offenses
were 30 % more likely to have a second offense (b =
-.357, p \ .001). In addition, the effect of family
criminal history was also significant at p \ .001.
Offenders with family criminal history background were
at a high risk for recidivism. On average, it took about
1.06 (SE = .01, 95 % CI 1.05–1.09) years for female
offenders to be referred again.
Delinquents Compared with Non-delinquents
As shown in Table 3, the two groups of juveniles (delinquent versus non-delinquent) were compared on four
demographic variables, including free lunch eligibility,
CPS services, foster care and disorder of aggression.
Descriptive statistics and results from the Chi square test
are summarized in the table. The two groups differed significantly on all four demographic variables. More than
50 % of juveniles were eligible for free lunch in both
groups; however, the percentage in the delinquent group
was larger than in the non-delinquent group. Overall, there
were more females from the delinquent group placed in
foster care (7 vs. 1.1 %), receiving CPS services (16.4 vs.
3.6 %) and diagnosed as having a disorder of aggression
(12 vs. .9 %) than from the non-delinquent group.
All four variables were included in the logistic regression model holding ethnicity as a constant. No significant
interaction effects were identified. The predictor coefficients for the final model are presented in Table 4. As
illustrated, female juveniles who had received CPS services
were 3.2 times more likely to be delinquent than those who
had not been placed in CPS (b = 1.171, p \ .001). Similarly, female juveniles who were placed in foster care were
two times more likely to be involved in delinquency
(b = .814, p \ .001). Eligibility for free lunch which
partially indicated the family’s socioeconomic status also
had a significant predictor effect. Female youths eligible
for free or reduced lunch were almost 1.4 times more likely
to become delinquent than those who were not eligible.
The strongest predictor of delinquency was a mental health
diagnosis related to a disorder of aggression. Female
Table 3 Background characteristics for delinquent and non-delinquent groups
Characteristics
Delinquent
group
Non-delinquent
group
Phi
coefficient
.151**
Free lunch
eligibility
Table 2 Cox proportional hazards regression with N = 34,580
Variable
b
SE
Chi
square
Exp(B)
21,776 (62.9 %)
17,818 (51.5 %)
No
12,838 (37.1 %)
16,796 (48.5 %)
CPS services
Yes
No
5,693 (16.4 %)
1,236 (3.6 %)
28,921 (83.6 %)
33,378 (96.4 %)
.096
.057
.094
1.100
Family delinquency
.234**
.056
17.510 \.001
1.264
Yes
2,439 (7 %)
Drug use history
.666**
.056
140.835 \.001
1.947
No
32,175 (93 %)
-.357**
.064
31.081 \.001
.700
Age at 1st referral
-.194**
.015
170.801 \.001
.824
Yes
4,154 (12 %)
328 (.9 %)
Family income
-.027
.056
.973
No
30,460 (88 %)
34,286 (99.1 %)
** p \ .001
123
.233
.629
.124**
Foster care
African American
Offense severity at
1st referral
2.801
p value
Yes
389 (1.1 %)
.078**
34,225 (98.9 %)
Disorder of
aggression
** p \ .001
.064**
J Child Fam Stud (2015) 24:427–433
Table 4 Logistic
delinquency
regression
431
coefficients
for
prediction
of
Predictor
R2
Free lunch
eligibility
.02**
.358**
CPS services
.05**
1.171**
.036
4.94**
3.226**
Foster care
Disorder of
aggression
.03**
.09**
.814**
2.415**
.062
.059
6.828**
13.334**
2.257**
11.188**
b
SE
.017
AORE
1.56**
AORF
1.430**
** p \ .001. R2 refers to Nagelkerke’s R2 following this step in the
equation and including the constant. Significance level for R2 is based
on the change in the log likelihood of the outcome. Significance level
for the Wald statistic is based on the final logistic regression equation.
B refers to the logistic regression coefficient in the final equation.
AORF refers to the adjusted odds ratio in the final equation. AORE
refers to the adjusted odds ratio at the initial time of entry
juveniles identified as aggressive were 11 times more likely
to be delinquent (b = 2.415, p \ .001) than those without
such a DSM-IV diagnosis. The SPSS analysis gave two
measures of R2, which were .09 (Cox and Snell R2) and .13
(Nagelkerke’s adjusted R2). These effect sizes are reasonably similar values and represent medium effects, according to Cohen (1988) (i.e., .02 for ‘‘small’’, .13 for
‘‘medium’’ and .26 for large).
Discussion
The present study provides overwhelming evidence of the
role of early social and psychological adversity in female
delinquency. Particularly important is the impact of mental
health disorders. In our study, females who had been
diagnosed with a mental health disorder involving impulse
control or aggression were approximately 11 times more
likely to commit a criminal offense than females who had
not been so diagnosed. While it is not possible to show a
direct causal effect of mental illness on delinquency, it is
important to recognize that in over 60 % of cases of female
delinquents with a diagnosis of a disorder of aggression,
the diagnosis of aggression preceded any involvement with
the juvenile justice system. The second most powerful
predictor of delinquency was child placement in CPS. The
role of attachment problems in the development of psychopathology among females has been previously noted
(Barrett et al. 2013c); in fact there is some evidence that
early disruptions in parent–child relationships may have
even more serious repercussions for females than for males
(Benda 2002).
Particularly important is the powerful influence of
removal from the home, whether in CPS or foster care,
even when child aggressiveness has been statistically
controlled. According to current systems theories (Granic
and Patterson 2011), child maltreatment (which is usually
untreated or undertreated) is likely to precipitate coercive
behaviors on the part of the child, behaviors that put the
young female at risk for delinquent behaviors. To the
extent that the pathways to delinquency may be somewhat
different for boys and girls, the needs of girls in terms of
prevention, treatment, and aftercare may also differ.
According to Quinn et al. (2005), the juvenile justice must
be prepared to address the needs of girls in a genderspecific, culturally competent manner. The overall success, however, will be dependent on the seamless service
delivery of agencies such as child welfare, mental health,
education (including special education), and juvenile justice (p. 137). With regard to recidivism, the present
findings are largely supportive of previous research on this
topic. It is interesting that the strongest predictor of female
recidivism in our study was a history of drug use, particularly given the strong association between adolescent
drug use and lack of parental control in the family
(Lamborn et al. 1991; Steinberg 2011). Also, consistent
with previous research (Barrett et al. 2010) are the findings that lower age at first referral and lower severity of
first offense are predictors of repeat offending. While the
latter finding might seem counter-intuitive, it may be due
in part to the fact that the most common status offense is
truancy and that failure to comply with mandatory attendance orders will automatically result in a referral for
contempt of court.
Interventions that help adolescent girls learn how to
manage their risk (e.g., effectively dealing with the trauma
of childhood physical and sexual assault) would be an
important contribution to the delinquency prevention field
(Ruffolo et al. 2004; see also Quinn et al. 2005). Additionally, interventions should focus on the protective factors that mitigate risk (Luthar 2006). Unfortunately,
females not only experience higher levels of mental health
problems than male peers, they are less likely to receive
treatment, and more likely to abandon treatment (Caufmann 2008). In addition, there is a paucity of gender specific programs that have empirical support to address
prevention and treatment-related challenges (Quinn et al.
2005). According to the General Accounting Office (2009),
in 2004 the Office of Juvenile Justice and Delinquency
Prevention (OJJDP) established a girls Study Group, funded by a $2.4 million multiyear contract with a research
institute, to identify ‘‘effective or promising programs,
program elements, and implementation principles’’ (p. 3).
The Study Group reported that 44 out of 61 girls’ delinquency programs had no empirical support and that
research findings on the remaining programs failed to
establish evidence of their effectiveness in preventing or
reducing girls’ delinquency (see also, Zahn et al. 2008b).
The failure to address the problem of female delinquency
123
432
continues to have serious repercussions; particularly given
the strong links between female delinquency and school
failure, teen pregnancy and child bearing and later mental
health problems (Barrett et al. 2013a).
There are several limitations to the present study which
may limit the generalizability of the findings. First, because
the sample is drawn from one state, South Carolina, the
sample may not be representative of a national sample.
State agencies follow specific procedures for determining
the need for CPS, referring children for mental health
services, and referring cases to the criminal justice system;
these procedures may differ from state to state (see for
example U.S. Department of Health and Human Services
2003). Thus, the empirical relationships identified in this
study between specific risk factors for delinquency and
later delinquent behavior might be magnified or attenuated
were the study to be conducted in other settings. A second
limitation is that information regarding the background
variables collected at intake on delinquent youth (including
drug use, income, and criminal history) were not available
for the control sample. Thus we were not able to include
these variables in the logistic regression analysis in which
we predicted membership in the delinquency group.
Inclusion of these variables might have altered the magnitude of the coefficients reported for this analysis. Third,
because of the very small number of females in the sample
whose ethnicity was other than African-American or
Caucasian, only these ethnic groups were included in the
analyses. Generalizations to individuals from other population groups must be made cautiously. Finally, we
emphasize that in the present study, in our examination of
family-related variables related to delinquency, we included only the most extreme indicators of family dysfunction
or disruption (CPS and foster care); closer examination of
the influences of parent–child interaction (trust, consistency, coercive behavior) would be useful in helping us
better understand how early social experiences contribute
to later female delinquent behavior.
With juvenile delinquency among females becoming
more pervasive in the United States, the task of identifying the school, home and psychological factors that lead to
female antisocial behavior has become more urgent. In the
present study we have elucidated the role of early family
disruption, and in particular removal from the home, in
female delinquency. Also, consistent with research on
male delinquents, the previous existence of mental health
problems appears to be the strongest predictor of female
delinquency, and, as indicated by the relationship between
drug use and repeat offending, a risk factor for later
offending. While research on effective programing for
females at risk for delinquent behavior has been limited,
the present study suggests that multi-systemic programs—
involving schools, communities, families and correctional
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J Child Fam Stud (2015) 24:427–433
settings—that address the young female’s need for consistent and nurturing relationships and that provide models
for academic and social success may be most helpful in
reducing, if not preventing, female delinquency.
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Juvenile Delinquency Recidivism: Are Black and White
Youth Vulnerable to the Same Risk Factors?
David E. Barrett and Antonis Katsiyannis
Eugene T. Moore School of Education, Clemson University
ABSTRACT: Using large-sample, archival data from the state o f South Carolina's juvenile justice
agency, we examine the question o f race differences in predictors o f repeat offending for a sample of
approximately 100,000 youth who had been referred for criminal offenses. Independent variables
relating to background, adverse parenting, mental health, school-related disabilities, and features o f
first offenses contributed to more than 25% o f the variance in recidivism for both Black and White
youth. Male gender, eligibility for free or reduced school lunch, diagnoses based on the Diagnostic
and Statistical Manual of Mental Disorders (4th ed.; text rev.; American Psychiatric Association,
2000), placement in Child Protective Services, and school identification as having a classification of
emotional or behavioral disorders or learning disabilities were all predictive o f juvenile recidivism.
In addition, early age o f first offense, status offenses, and being prosecuted for a first offense were
significant predictors. Magnitudes o f prediction were similar across racial groups, suggesting similar
vulnerability o f both Black and White youth to these early adversities. Interactions between race and
other independent variables accounted for only .001 o f the variance in recidivism. However, there
were several significant interactions. Mental health history and characteristics o f the first offense
were stronger predictors for White youth than for Black youth. Gender, poverty (free or reduced
lunch), and school identification o f having a classification o f emotional or behavioral disorders were
stronger predictors for Blacks than for Whites. Implications for prevention are addressed.
■ The overrepresentation of Black youth in
the juvenile justice system has been a persistent
concern and is evident across contact points
(National Council on Crime and Delinquency,
2007). Research studies on m inority arrests
and confinement attest to race effects, direct or
indirect, at m ultiple stages of juvenile justice
processing and across jurisdictions (Pope,
Lovell, & Hsia, 2001; see also Huizinga et
al., 2007). For example, in 2010, 1,642,600
juveniles were arrested in the United States for
violent crimes. In the same year, the racial/
ethnic composition for juveniles ages 10 to 17
was 76% White, 17% Black, 5% Asian/Pacific
Islander, and 1% American Indian. However,
the racial/ethnic composition for juvenile
arrests was 47% White, 51 % Black, 1% Asian,
and 1% American Indian. Proportions of Black
offenders were 56% for murder, 67% for
robbery, 42% for motor vehicle theft, 41%
aggravated assault, 38% simple assault, 36%
for forcible rape, 36% for burglary, and 36%
for weapons (Puzzanchera, 2013). Further­
more, data on cumulative prevalence arrest
rates show that Black youth are almost 50%
more likely than W hite males to have been
arrested at least once by the age of 18 (Brame,
Bushway, Paternoster, & Turner, 2014).
184 / May 2015
In addition to race differences in arrest
rates, there is also evidence of race disparities
in adjudication (prosecution and referral to
a fam ily court), w ith Black youth more likely
than W hite youth to be prosecuted for serious
crimes (Barrett, Katsiyannis, & Zhang, 2010).
Black juveniles are more likely to be sent to
secure confinement than W hite juveniles and
are more likely to be transferred to adult
facilities (The Sentencing Project, 2014). Fi­
nally, Black students are also disproportion­
ately represented in disciplinary proceedings
in the schools, accounting for 27% of law
enforcement referrals and 31% of schoolrelated arrests (U.S. Department of Education,
2014; U.S. Department of Education, Office of
Civil Rights, 2014a, 2014b).
To address disproportionate m inority con­
finement (DMC), the Office of Juvenile Justice
and Delinquency Prevention (OJJDP) adminis­
ters the Formula Grants program under Title II,
Part B, of the Juvenile Justice and Delinquency
Prevention (JJDP) Act of 1974. Based on the
most recent amendments to the JJDP Act of
1992, the need to address this problem was
elevated to a core requirement, w ith 25% of
funds linked to state compliance. Currently,
based on the OJJDP's five-phase DMC
Behavioral Disorders, 40 (3), 184-195
Reduction Model (identification, assessment/
diagnosis, intervention, evaluation, and mon­
itoring), 41 states have DMC subcommittees
under their state advisory groups (18 full-time
and 37 part-time DMC coordinators), 34 states
have invested in targeted local DMC-reduction
sites, 30 states have implemented nationally
recognized models, and 39 states have pro­
vided a timeline for tracking and monitoring
trends over time (OJJDP, 2012). Meta-analyses
regarding the effectiveness of these programs
have indicated positive intervention effects for
minority youth; outcomes include a reduction
in delinquent behavior and improvement in
school participation, peer relations, academic
achievement, and psychological adjustment.
But particularly striking is the finding that the
programs have been equally effective for
minority and White juveniles. In their review
of over 300 studies, including studies prior to
1992, Wilson, Lipsey, and Soydan (2003)
compared method-adjusted effect sizes (in­
tervention effect sizes controlling for method­
ological differences between studies) for
minority (primarily Black) and White youth;
with method controlled, race differences in the
magnitude of intervention effects were non­
significant.
The finding of similar effects of interven­
tions on minority and nonminority delinquents
raises the question of vulnerability and re­
silience among minority youth. The fact that
targeted interventions have similar effects for
Black and White youth suggests that youth of
different backgrounds are vulnerable to the
same risk factors and benefit from the same
reparative experiences. Thus, we might expect
that for both White and Black youth, early
experiences such as parental neglect or abuse,
mental health problems, and school failure
would be predictive of delinquency (Barrett,
Katsiyannis, Zhang, & Zhang, 2014) and that
multisystemic interventions focusing on the
family environment, the school experience,
and the child's individual social adjustment
would be ameliorative (Farmer & Farmer,
2001 ).
Another possibility, however, is that mi­
nority youth, and in particular Black youth, are
more resilient in the face of early adverse
experiences. The concept of Black resilience is
compelling (APA Task Force on Resilience and
Strength in Black Children and Adolescents
[APA Task Force], 2008) and has received both
popular and empirical support. For example,
family instability appears to be a stronger
Behavioral Disorders, 40 (3), 184-195
predictor of behavioral and academic prob­
lems among White children than Black chil­
dren (Fomby & Cherlin, 2007). Also, there is
evidence that family structure transitions (e.g.,
parental separation) have more deleterious
consequences for White than Black children
with regard to early sexual activity among
females (Wu & Thomson, 2001). Finally, there
is evidence that Black youth tend to have
higher overall self-esteem than Whites, partic­
ularly with regard to their satisfaction with
body image (Steinberg, 2014), a fact that has
been linked with the lower rates of presenta­
tion of weight concerns (e.g., anorexia ner­
vosa) among Black Americans than White
Americans (American Psychiatric Association
[APA], 2013). Multiple explanations have been
given to explain a lower vulnerability to
certain social-environmental stressors among
Black youth. Fomby, Mollborn, and Sennott
(2010) suggested two bases for this resilience:
inclusion in broader social networks (including
church, peer group, and neighborhood) and
importance of kin relationships (i.e., extended
family).
But the concept of resilience needs to be
examined carefully. An assumption of greater
resilience among minority populations may
lead to an underestimation of the impact of the
very factors that place a population "at risk."
This is a particular concern if the early adverse
experiences have the same or even greater
impact on vulnerable populations. The com­
peting hypothesis (vs. resilience) is increased
vulnerability. Vulnerability would result when
weakened bonds between youth and main­
stream institutions, such as the school, and
adverse early experiences result in poorer self­
perceptions and efficacy, variables which
themselves are predictive of more persisting
behavior problems among children in multi­
ple-risk families (Radke-Yarrow & Brown,
1993). A third possibility is that the magnitudes
of effects of known risk factors for later
behavior problems are very similar for White
and minority youth.
In the present study we examine the role of
multiple categories of risk factors for juvenile
delinquency recidivism for large samples of
Black and White youth who had had a least
one referral to a state juvenile justice system.
Independent variables
include
parental
maltreatment (foster care, referral to Child
Protective Services [CPS]), mental health
problems (referral to state department of
mental health), and school-related disability
May 201 5/1 8 5
(learning disability [ED], emotional/behavioral
disorder [EBD]). The outcome of interest was
juvenile recidivism: a second referral to the
state juvenile justice system. In addition to
testing for race differences in the above risk
factors, we also tested for race differences in
the role of gender and age of first offense,
variables known to be highly predictive of
juvenile offending and recidivism (Barrett
et al., 2014).
Method
Source of Data
Data for this study were obtained from two
sources, the South Carolina Department of
Juvenile Justice (DJJ) and the South Carolina
Budget and Control Board's Office of Research
and Statistics (ORS). DJJ data comprised in­
formation on approximately 100,000 youth
who had been born in the period of 19811988 and who had been involved in de­
linquent activity. We linked the DJJ data with
data obtained from the ORS. The ORS houses
data from all of the state agencies in South
Carolina, including, but not limited to, the
South Carolina Department of Education
(SDE), the South Carolina Department of
Social Services (DSS), the South Carolina
Department of Mental Health (DMH), and
the South Carolina DJJ. These linkages
enabled us to examine environmental influ­
ences on delinquency and recidivism using
data that were not available in the original DJJ
file.
D/J Data
Data were drawn from the South Carolina
DJJ Management Information System. The DJJ
sample consists of all juveniles born between
1981 and 1988 whose cases were referred to
the South Carolina DJJ on at least one occasion
("referral"). The sample was part of a m ulti­
cohort, matched control study conducted in
conjunction with the South Carolina Budget
and Control Board (Barrett et al., 2014), a study
that also included nondelinquent youth. In
South Carolina cases are first processed at the
fam ily court level by the DJJ. Intake workers
from the DJJ assess risk and needs and forward
cases to the Solicitor's Office w ith advisory
recommendations (e.g., diversion or prosecu-
1 8 6 /M a y 2015
tion). If the case is prosecuted, the juvenile
may be committed to the custody of the DJJ,
given probation, or given another penalty,
such as a school attendance order.
The 1981-1988 cohorts include 99,602
individuals, 65,502 (65%) males and 35,100
(35%) females. The racial composition is
50,496 (51%) Black, 47,537 (48%) W hite, and
1,569 (2%) other (Asian and Hispanic). The
average age of the juveniles when they were
first referred to the system was 14.47 years (SD
= 1.94), and the mean total number of referrals
per juvenile was 2.21 (SD = 2.00). O f the
99,602 juveniles, 54% had one referral only,
19% had tw o referrals, and 27% had three or
more referrals. Social demographic data were
collected selectively by the DJJ and were
available for only about half of the sample
(see Barrett et al., 2010, for details). For
purposes of the present study, only Black and
W hite youth were considered in the analyses.
Individual data on delinquency history
were aggregated for each subject in the sample.
Data available for each subject included age at
first offense, severity of first offense, and severity
of second offense, if applicable. Data on
dispositions (penalties) were also collected.
The determination of the seriousness of a crime
was based on the coding scheme employed by
South Carolina. The DJJ rates crimes on an
ordinal scale, w ith lower ratings representing
less serious offenses. For purposes of this
analysis, we categorized offenses as status
offenses (DJJ severity levels 1 and 1.5) and
nonstatus offenses (rating levels of 2 and above).
OKS Data
For all individuals in the DJJ sample and
also for the matched control group (described
below), data from other state agencies (housed
in the ORS) were made available. Files on
each child in the DJJ file were linked w ith files
of the other state agencies using a probabilistic
matching algorithm. In the ORS linkage
system, once a match is identified, an ID
number is assigned. The same ID is used for all
subsequent episodes of services, regardless of
data source or service provider. Additional
information about the key linkage system is
available on request.
For the present analyses, individual data in
the DJJ files were linked w ith data for the same
individuals from the DSS, DM H, and SDE.
Data obtained from the DSS included in­
formation about foster care placements and
Behavioral Disorders, 40(3), 184-195
whether or not an individual had ever been
placed in the custody of CPS. For foster care,
information about age and duration of place­
ment and number of placements was obtained.
With respect to CPS, we obtained information
about the reason for and timing of CPS. Data
obtained from the DMH included information
about age at first, second, and most recent
referrals and primary diagnosis based on the
Diagnostic and Statistical Manual o f Mental
Disorders (4th ed.; text rev.; DSM-IV-TR; APA,
2000) at each referral. Primary diagnoses were
further categorized into seven major categories
(described in Analyses section). Data from the
SDE included information about the ages at
which the student was eligible for free and/or
reduced lunch and eligibility for special
education services due to LDs or EBDs. After
separate files were constructed for each
agency (D)J, DMH, DSS, DOE), files were
merged to create a new master file for the DJJ
sample.
Analyses
We used a series of logistic regression
analyses to examine the individual and com­
bined influences of selected categories of
independent variables on juvenile delinquen­
cy recidivism; that is, presence of referral to
the DJJ for a second offense. Our analysis
involved three steps. First, we obtained de­
scriptive statistics on all independent variables
and on the dependent variable for each of the
two comparison groups, Black and White. We
also conducted cross-tabulations and calculat­
ed chi-squared statistics to examine the re­
lationship between ethnic group and each of
the categorical predictor variables (we con­
ducted an independent sample f test on the
one continuous variable, age of first referral).
Second, we carried out a multivariable
logistic regression analysis using the whole
sample, including interaction terms for the
interaction of racial group and each in­
dependent variable. The inclusion of in­
teraction terms enabled us to test for the
possibility of significant differences in the
logistic regression coefficients for indepen­
dent variables for Black versus White youth.
Youth incarcerated for the first referral were
excluded from the analysis. In the logistic
regression analysis we included six blocks of
predictors. In predicting the variable recidi­
vist (vs. nonrecidivist), we first examined the
role of demographic variables. Included in
Behavioral Disorders, 40 (3), 184-195
this block were the variables eligible for free
or reduced lunch (coded Yes or No), race,
and gender. The second block of predictors
included two measures of family background/dysfunction, placement in foster care
(Yes or No) and placement in CPS (Yes or
No). The third set of predictors focused on
childhood psychopathology. In constructing
these variables, all DSM-IV-TR diagnoses
conferred by the DMH were assigned to one
of seven categories. Category assignments
were made by the first author, a licensed
psychologist, in consultation with collea­
gues. The categories used were aggression
and conduct problems; drug-related prob­
lems; attention and learning disorders, men­
tal retardation and, other problems starting in
childhood; mood and anxiety disorders;
psychotic disorders; adjustment and milder
disorders; and other serious disorders. For
the present analysis, subjects were first
scored for presence or absence (at any time
in development) of a primary diagnosis
involving aggression and/or conduct prob­
lems. The DSM-IV-TR classifications that
were used to define an aggressive behavior
problem included antisocial personality dis­
order (DSM-IV-TR classification 301.7), im­
pulse control disorder (312.30), conduct
disorders (312.81, 312.82, 312.89), disrup­
tive behavior disorder (312.9), oppositional
defiant disorder (313.81), and child or
adolescent antisocial behavior (V71.02;
APA, 2000). They were then scored for
presence or absence of a primary disorder
involving any other type of disorder recog­
nized in the DSM-IV-TR. These two vari­
ables constituted the third block of
predictor variables. The fourth set of vari­
ables included two indicators of eligibility
for special education. Subjects were first
scored for presence or absence of a schoolbased identification as eligible for special
education services due to an LD. They were
also scored for presence or absence of
a school-based identification as eligible for
services due to an EBD. The fifth block of
predictors included the variables age at first
offense (continuous variable), severity of first
offense (status offense versus misdemeanor
or felony), and prosecuted for first violation
(Yes or No). The final block of predictors
included all two-way interaction terms (e.g.,
Race X Gender). We also ran simple logistic
regression analyses for each of the indepen­
dent variables to examine the relationship
May 2015 / 187
between the independent variable and re­
cidivism with other independent variables
uncontrolled.
Third, we repeated the logistic regression
analyses but this time for Black and White
youth separately. We again entered variables
in blocks. Blocks were the same as those
described in the whole sample analyses, with
two exceptions: (a) The first block did not
include the variable race, and (b) interaction
terms were not included.
TABLE 1
Descriptive Statistics for Predictor Variables
in Relation to Ethnicity
Total Sample
Black
(N =
96,613 )
(n =
W hite
49 ,7 1 0 )
(n = 46,903 )
Gender (Male)
64.58%
64.14%
65.04%
Receives free or
reduced lunchi
61.73%
75.26%
49.39%
Variables
Demographic
Parenting
Results
Foster care
CPS
Descriptive Statistics
Percentages of individuals manifesting
different background characteristics and risk
indicators are shown in Table I for the entire
sample and for Black and White youth
separately. As shown in Table 7, White offen­
ders were more likely than Black offenders to
have been diagnosed with a DSM-IV-TR
disorder not associated with aggressive or
impulsive behavior. Black youth were more
likely than White youth to receive free or
reduced lunch in the school system, to have
been in foster care or CPS, to have been
diagnosed with a DSM-IV-TR disorder relating
to aggression or impulse control, and to have
been identified by a school as having a classifi­
cation of an EBD. White offenders were more
likely than Black offenders to have been
prosecuted for their first offense and were more
likely to be male. Black youth had a lower mean
age of first referral and were less likely than
White youth to have been first referred for
a status offense.
5.62%
5.84%
4.66%
12.27%
13.00%
11.49%
DSM-IV-TR
diagnoses
Aggression
14.51%
16.56%
12.32%
Other
25.59%
22.12%
29.24%
16.77%
16.66%
16.89%
5.62%
6.31%
4.90%
Disabilities
LD
EBD
Delinquency
history
M = 14.45
(1.95)
M = 14.20
(2.06)
M=14.73
(1.79)
First offense
adjudicated
21.02%
19.81%
22.31%
Nonstatus
offense
79.30%
79.87%
79.10%
Age at first
referral
Note. Chi-squared analyses for differences between racial
groups were conducted for all categorical variables.
Significant differences at p < .001 were detected for all
comparisons except Gender Male and LD. Comparison for
Gender Male was significant at p < .01; LD was not
significant. For Age at first referral, f(96/ 611) = 42.92 (p <
.001), standard deviations are shown in parentheses. Sample
sizes for Nonstatus Offense are n = 46,871 (Black) and n =
49,686 (White). CPS = Child Protective Services, LD =
learning disability, EBD = emotional or behavioral disorder.
Logistic Regression
Whole Sample: No Interaction Terms
Table 2 shows results of the multivariable
logistic regression analysis with interaction
terms not included, as well as results of simple
logistic regression analyses. The multivariable
analysis showed significant effects for race and
gender with Black youth and males more
likely than White youth and females to commit
a second offense, x20 , N = 96,557) = 39.74,
p < .001, and x2 = 494.08, p < .001,
respectively. There was also an effect for free
or reduced lunch with youth qualifying for free
or reduced lunch more likely to commit
a second offense (%2 = 709.10, p < .001).
188 / May 2015
While the effect for foster care was not
significant, there was a significant effect for
CPS (x2 = 279.04, p < .001). Youth who had
been in CPS were approximately 50% more
likely than those who had not been in CPS to
commit a second crime. Mental health di­
agnosis was significantly related to the likeli­
hood of a second offense, with youth with
either a diagnosis relating to aggressive behav­
ior or any other diagnosis more likely to be
referred for a second offense (x2 = 2873.15, p
< .001, and x2 = 1353.62, p < .001,
respectively). The values for adjusted odds
ratios (AORs) show that youth with mental
Behavioral Disorders, 40 (3), 184-195
TABLE 2
Logistic Regression Analysis for Prediction of Recidivism (N = 96 ,557a)
Block
Block 1
Variable
R 2 Block
Race (Black)
Gender (Male)
Free or reduced lunch
Block 2
Foster care
CPS
Block 3
.17**
EBD
LD
Block 5
.08**
DSM-IV-TR Aggressive
DSM-IV-TR Other
Block 4
.05**
1?**
Age first offense
Severity
Adjudicated
.26**
B
AORE
AORf
0.10
39.74**
1.33**
1.10**
0.35
494.08**
1.34**
1.42**
0.42
709.10**
2.09**
1.53**
0.07
3.41
3.03**
1.07
W ald (x2i)
0.43
279.04**
2.70**
1.54**
1.21
2873.15**
4.79**
3.34**
0.64
1353.62**
2.51**
1.91**
0.36
110.77**
3.19**
1.44**
0.10
22.72**
1.55**
1.10**
-0 .3 0
5263.94**
0.71**
0.74**
-0 .3 4
367.04
0.62**
0.71**
852.36**
1.96**
1.70**
0.53
Note. R2 block refers to Nagelkerke R2 following this step in the equation and including the constant. Significance level for R2
block is based on the change in the log-likelihood of the outcome. Significance level for the Wald statistic is based on the final
logistic regression equation. B refers to the logistic regression coefficient in the final equation. AORE refers to the adjusted odds
ratio if entered alone in the equation. AORF refers to the adjusted odds ratio in the final equation. CPS = Child Protective
Services, DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision, LD = learning
disability, EBD = emotional or behavioral disorder.
“ Sample size is reduced to 96,557 for multivariable analysis due to missing observations on Severity.
**p < .001.
health diagnoses relating to aggressive behav­
ior were more than 3 times more likely to
commit a second offense than other first
offenders and that youth with another mental
health diagnosis were almost twice as likely as
nondiagnosed youth to commit a second
offense. Youth identified as eligible for special
education services due to an EBD or LD were
more likely to commit a second offense than
youth without these special education classi­
fications (x2 = 110.77, p < .001 and x2 =
22.72, p < .001, respectively). Finally, there
was a significant relationship between age of
first offense and recidivism (x2 = 5263.94, p <
.001). The AOR of .76 shows that for each year
of reduced age of first offense, the odds of
a second offense increase by approximately
25%. In addition, youth who had been referred
for status offenses and youth adjudicated for
their first offense were more likely to commit
a second offense (x2 = 367.04, p < . 001 and
X2 = 852.36, p < . 001, respectively). Foster
care was not significantly related to recidivism.
The total adjusted R2 was .26; model x2(12, /V
= 96,557) = 20482.90, p < .001. Also, as
shown in Table 2, simple logistic regression
analyses showed that each independent vari­
able when considered alone was a significant
predictor of recidivism; AOREshows values of
Behavioral Disorders, 40 (3), 184-195
adjusted odds ratios with only one variable in
the equation.
Whole Sample: Interactions
When all two-way interaction terms were
added to the equation at step six, the total value
of R2 was only slightly increased from .256 to
.257, x2(11, N = 96,557) = 159.99, p < .001.
Six interactions were significant: Race X Gender
(X2 = 11 -83, p = .001), Race X Free Lunch (x2 =
34.30, p < .001), Race X DSM-IV-TR NonAggressive (x2 = 22.38, p < .001), Race X EBD
(X2 = 21.43, p < .001), Race X Severity (x2 =
14.99, p < .001), and Race X Adjudication
(X2 = 31.60, p < .001). These interactions are
explained in the following section.
Split Files: Analyses by Racial Group
Tables 3 and 4 show the results of the
logistic regression analyses for White and
Black youth considered separately. Values of
R2 were .25 for White youth and .26 for Black
youth. As with the analysis for the total sample,
for both groups all variables except foster care
were significant predictors of recidivism in the
multivariable analysis, and all variables were
significant in the simple logistic regression
May 2 0 1 5/1 8 9
TABLE 3
Logistic Regression Analysis for Prediction of Recidivism for White Youth (N = 46,871a)
Block
Block 1
Variable
Free or reduced lunch
Block 2
Block 4
AORe
1.16**
1.35**
0.34
240.88**
2.16**
1.40**
0.08
2.03
3.21**
1.08
.08**
0.42
122.07**
2.90**
1.52**
1.17
1208.22**
4.62**
3.23**
.16**
0.72
917.57**
2.79**
2.06**
0.20
15.31**
2.65**
1.22**
0.08
8.78*
1.55**
1.09*
.05**
EBD
LD
Block 5
AORe
164.13**
DSM-IV-TR Aggressive
DSM-IV-TR Other
B
0.30
Foster care
CPS
Block 3
R2 Block
Gender (Male)
.15**
W ald ( * 2,)
Age first offense
-0 .3 0
2234.72**
0.71**
0.75**
Severity
-0 .4 0
262.27**
0.56**
0.67**
0.63
594.21**
2.37**
1.87**
Adjudicated
.25**
Note. R2 block refers to Nagelkerke R2 following this step in the equation and including the constant. Significance level for R2
block is based on the change in the log-likelihood of the outcome. Significance level for the Wald statistic is based on the final
logistic regression equation. B refers to the logistic regression coefficient in the final equation. AOR e refers to the adjusted odds
ratio if entered alone in the equation. AORF refers to the adjusted odds ratio in the final equation. CPS = Child Protective
Services, DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision, LD = learning
disability, EBD = emotional or behavioral disorder.
aSample size is reduced to 46,871 for multivariate analysis due to missing observations on Severity.
*p < .01.
**p < .001.
analyses. In addition, the relative magnitudes
of the different predictors were very similar
across groups; in fact, using independent
variable as the unit of analysis, the correlation
between adjusted odds ratios (across the two
groups) was .95 (f9 =9.13, p < .001).
Significant interactions in the total sample
analysis indicated differences in the magnitude
of six predictor variables between the two
groups. Significance tests are based on differ­
ences in the magnitude of logistic regression
coefficients (Paternoster, Brame, Mazerolle, &
Piquero, 1998). Three variables were stronger
predictors for White youth than for Black.
Having a DSM-IV-TR diagnosis for a disorder
not described as relating to aggression or
impulse control was a stronger predictor for
White youth than for Black (Z = 4.72, p < .001).
Also, having been referred first for a status
offense and having been prosecuted for the
first offense were stronger predictors for
Whites than for Blacks (Z = 3.88, p < .001
and Z = 5.55, p < .001, respectively). In
contrast, being male, being eligible for free or
reduced lunch, and having been classified by
a school as eligible for special education due
to an EBD were all stronger predictors of
recidivism for Black than White youth. Results
of significance tests were Z = 3.46, p < .001
for gender; Z = 5.81, p < .001 for free or
190 / May 2015
reduced lunch; and Z = 4.65, p < .001 for
EBD.
Summary of Results
Independent variables relating to back­
ground, adverse parenting, mental health,
school-related disabilities, and features of first
offenses contributed to more than 25% of the
variance in recidivism for both Black and
White youth. Male gender, eligibility for free
or reduced school lunch, DSM-IV-TR diagno­
ses, placement in CPS, and identification as
qualifying for special education services due to
an EBD or LD were all predictive of juvenile
recidivism. In both groups, a mental health
diagnosis relating to aggression or impulse
control was the strongest predictor of recidi­
vism. In addition, early age of first offense,
status offenses, and being prosecuted for a first
offense were significant predictors. Magni­
tudes of prediction were similar across ethnic
groups. While interactions between race and
other independent variables accounted for
only .001 of the variance in recidivism, there
were several significant interactions. Mental
health history and characteristics of the first
offense were stronger predictors for White
youth than for Black youth. However, gender,
Behavioral Disorders, 40 (3), 184-195
TABLE 4
Logistic Regression Analysis for Prediction of Recidivism for Black Youth (N = 49,710)
Block
Block 1
Variable
I f Block
Free or reduced lunch
Block 2
Block 3
1.51**
.04**
0.52
483.02**
1.88**
1.69**
0.08
2.37
2.82**
1.09
.08**
0.45
163.52**
2.50’ *
1.58**
1.24
1669.04**
4.80**
3.45**
0.56
458.06**
2.44**
1.75**
0.52
117.39**
3.66**
1.68**
.16**
EBD
LD
Block 5
AORf
1.62**
DSM-IV-TR Aggressive
DSM-IV-TR Other
Block 4
AOR e
356.42**
Foster care
CPS
.17**
Age first offense
Severity
Adjudicated
B
0.41
Gender (Male)
.26**
W ald (x2i)
0.11
16.01**
1.56**
1.12**
-0.31
3022.93**
0.71**
0.74**
-0 .2 6
100.57**
0.64**
0.77**
0.42
269.48**
1.66**
1.52**
Note. R2 block refers to Nagelkerke R2 following this step in the equation and including the constant. Significance level for R2
block is based on the change in the log-likelihood of the outcome. Significance level for the W ald statistic is based on the final
logistic regression equation. B refers to the logistic regression coefficient in the final equation. AORE refers to the adjusted odds
ratio if entered alone in the equation. AORF refers to the adjusted odds ratio in the final equation. CPS = Child Protective
Services, DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision, LD = learning
disability, EBD = emotional or behavioral disorder.
“Sample size is reduced to 49,710 for multivariate analysis due to missing observations on Severity.
* * p < . 001.
poverty (free or reduced lunch), and school
classification of an EBD were stronger pre­
dictors for Blacks than for Whites.
Discussion
It is w ell recognized that early adverse
experiences in the fam ily and in school are
strongly linked to later child behavior prob­
lems (Dodge, Greenberg, Malone, & Conduct
Problems Prevention Research Group, 2008).
Recognized also is the higher prevalence of
fam ily systems disruption and school failure
among Black children (Fomby et al., 2010;
Steinberg, 2014), factors that help explain in
part the disparities in behavioral outcomes,
including both prosocial behaviors, such as
school achievement, and antisocial outcomes,
such as juvenile delinquency, between W hite
and Black youth. The findings of race dispar­
ities in achievement, mental and physical
well-being, and antisocial behavior have
motivated both scholars and clinicians to
examine further the sources of strength and
resilience among Black children and families
(APATask Force, 2008). Empirical studies have
identified a number of protective factors that
may lim it the deleterious effects of early
adversity, including institutional barriers to
Behavioral Disorders, 40 (3), 184-195
social and emotional well-being. Protective
factors include individual factors, such as
emotional regulation (Mendez, Fantuzzo, &
Cicchetti, 2002); fam ily influences, such as
closeness to parents (Bynum & Kotchik, 2006);
school variables, such as authoritative teachers
(Ladson-B i 11i ngs, 1994); and comm unity fac­
tors, such as quality childcare (Gottfredson,
Gerstenblith, Soule, Womer, & Lu, 2004). The
APA Task Force (2008) provided a detailed
review of the literature.
But studies on individual differences in
resilience do not address the broader question
of whether known risk factors for later de­
velopmental problems— including risk factors
relating to early adversity in the home envi­
ronment, mental health, school learning prob­
lems, and demographic factors such as gender
and social-economic status— have similarly
deleterious effects for W hite and m inority
youth. Are Black youth as a group protected
against social-environmental and personal risk
factors by virtue of having experienced social
and economic adversity in the past and
learning how to adapt to m ultiple stressors?
O r are Black youth even more vulnerable than
W hite youth to these early environmental risk
factors, perhaps due to the same early experi­
ences and unique socio-cultural contexts that
are often seen as protective?
May 2 0 1 5 / 191
The results of our study provide a mix of
conclusions. The most striking finding in the
present study is the extraordinary concordance
in the effects (and magnitudes of effects) of
early risk indicators for later antisocial behav­
ior, measured in this study by the presence of
juvenile delinquency recidivism. For large
samples of both Black and White youth who
had been referred to the DJ) on at least one
occasion, juvenile recidivism (referral for
a second offense) was significantly predicted
by the same set of variables: gender, poverty,
being referred to CPS, having a DSM-IV-TR
diagnosis for a psychological disorder, being
identified as eligible for special education
services due to an EBD or LD, having an
earlier age of first offense, committing a status
offense as a first offense, and being prosecuted
for a first offense. In addition, the amount of
variance in recidivism accounted for by these
predictor variables was nearly identical: 25%
of the variance in the White sample and 26%
of the variance in the Black sample. Finally,
AORs for the two groups were highly corre­
lated; with type of predictor variable as the
unit of analysis, the correlation in the magni­
tudes of AORs was .95.
But there was also evidence of differential
effects of individual risk factors for Black
versus White youth. Recognizing that interac­
tions between race and individual predictor
variables accounted for only .001 of the
variance in recidivism in the total sample, it
was still the case that the increase in variance
accounted for by interaction was significant.
Comparison of logistic regression coefficients
for the Black and White samples showed six
variables where the magnitudes of effects
differed for the two groups: gender, poverty,
and EBD classifications were stronger predic­
tors for Black youth; DSM-IV-TR diagnoses
(nonaggressive), status offending, and prose­
cution for first offense were stronger predictors
among Whites.
Several of the interactions identified are
difficult to interpret. Earlier analyses (Barrett,
Katsiyannis, & Zhang, 2006; Barrett et al.,
2010) indicated that status offenses are more
likely than nonstatus offenses to be adjudicat­
ed and to lead to a second offense. Why the
relationship between status offending and
recidivism should be stronger for one ethnic
group than another is not clear. Similarly the
findings of a stronger link between nonaggres­
sive (i.e., internalizing) disorders and recidi­
vism among White youth and a stronger
192 / May 2015
relationship between gender and recidivism
among Black youth merit further attention.
On the other hand, the interactions in­
volving poverty (free or reduced school lunch)
and EBD are more readily interpretable. For
Black youth in particular, qualifying for free or
reduced lunch was a risk factor for recidivism.
Since poverty is also associated with lower
family and neighborhood stability, this finding
is inconsistent with a view that due to
adaptation to difficult environments, Black
youth may develop a special resilience in
dealing with poverty. Rather, poverty appears
to place a particularly heavy burden on Black
youth. Findings relating to EBDs are notewor­
thy. A school's identification of a child as
eligible for special education due to an EBD is
a stronger predictor of juvenile recidivism for
Black youth than for White, and is also
a stronger predictor of recidivism than identi­
fication as eligible due to an LD. It is important
to recognize that Black students are dispro­
portionately represented in special education,
particularly in the disability categories of
EBDs, a finding supported by the present data,
and intellectual disabilities (ID; Kauffman,
Simpson, & Mock, 2009).
Black students receiving special education
services are also less likely to be placed in the
general education classroom, resulting in
resegregation of the population (Artiles, Kozleski, Trent, Osher, & Ortiz, 2010). Further,
Black students with (and without) disabilities
are at greater risk than White peers to be
expelled or suspended (U.S. Department of
Education, Office of Civil Rights, 2014a); in
fact, one out of six Black students in public
school has been suspended at least once
(Losen & Gillespie, 2012) and 59% of Black
males have been suspended or expelled from
school, compared to 24% of White males
(Toldson, 2011). Importantly, Black students
with disabilities account for a disproportionate­
ly high percentage of disciplinary inclusions
totaling 10 days or more (IDEA Data Center,
2014).
Finally, Black students, particularly males,
are also dropping out of school at almost twice
the rate for White students (Chapman, Laird, &
KewalRamani, 2010; National Center for
Education Statistics, 2013). Regarding academ­
ic performance, in 2012 the percentage of 17year-old Black students at or above the
National Assessment of Educational Progress
(NAEP) reading score level of 300 (able to find,
understand, summarize, and explain relatively
Behavioral Disorders, 40 (3), 184-195
complicated literary and informational mate­
rial) was 22% versus 47% for White students.
Similarly, percentages of mathematics scores
of 300 or higher (able to perform reasoning
and problem solving involving fractions, dec­
imals, percentages, elementary geometry, and
simple algebra) were 33.8% for Black students
versus 70.3% for Whites; the disparity was
even more pronounced (1.1% for Black
students vs. 9.1% for White) for scoring 350
or above (able to perform reasoning and
problem solving involving geometry, algebra,
and beginning statistics and probability; Na­
tional Center for Education Statistics, 2013).
Given these disparities in academic and
behavioral outcomes, it is important that
schools examine closely the methods used to
identify students' educational needs, including
the procedures that are used to identify
children as having EBDs.
Limitations and Conclusions
A major limitation in the present study was
that while we were able to include a general
measure of poverty in our analyses, more
specific indicators of socioeconomic disadvan­
tage, such as parental characteristics, family
history, and family income were not included.
As noted previously, the South Carolina
DJI does obtain more specific socio-demo­
graphic data (e.g., persons living in the
family, family income) on youth referred to
the DJJ; this information is collected at intake.
However, this background information is
collected selectively; in general, background
data are more likely to be collected in cases
where the DJJ intake worker sees the situation
as more serious. Because of this potential
bias, we chose to include an SDE variable—
eligibility for free or reduced lunch—that was
available for all subjects. In addition, our
definition of recidivism—presence of a second
referral to the DJJ—was very broad. W hile it
would have been possible to use a more
stringent measure of recidivism (e.g., pres­
ence or absence of a felony at some time after
the first violation), we recognized that be­
cause more serious violations are less com­
mon, we would probably account for a lower
percentage of the variation in recidivism if we
made this decision; for this reason (the
decision to increase variability in the out­
come variable), we chose to examine only
whether there was or was not a repeat
offense.
Behavioral Disorders, 40 (3), 184-195
Despite these limitations, our findings
suggest a number of conclusions and lead to
several directions for further research. First, the
risk factors for Black and White youth for
juvenile delinquency recidivism are remark­
ably similar. Many scholars have noted the
ways in which Black youth appear, in some
ways, more resilient than White youth in
dealing with the challenges of adolescence.
Black youth have higher self-esteem than
White youth; this is particularly true for girls
(Biro, Striegel-Moore, Franko, Padgett, & Bean,
2006). Further, as discussed earlier, Black
youth appear to tolerate variations in family
structure that White youth do not. But these
findings should not lead us to underestimate
the impact of early adverse experiences— in
the family, neighborhood, or school— on Black
children. Individual children, regardless of
ethnic/racial group, often show great resilience
in coping with difficult circumstances. Chil­
dren with strong peer relationships, realistic
and positive views of themselves, and good
decision-making skills are more able than
others to overcome multiple stressors and
adversities (Radke-Yarrow & Brown, 1993),
and among today's Black youth it is critical
that these abilities and relationships be sup­
ported (APA, 2008). But all children, both
minority and nonminority, are likely to be
negatively affected by the types of early
adverse experiences we have examined in this
study.
Second, one area where Black youth may
be particularly impacted by adversity is the
area of special education. Not only are Black
children overrepresented in special education
classifications, identification as having an EBD
for Black children is a stronger predictor of
juvenile delinquency recidivism than it is for
White children. This is true when factors such
as poverty, family disruption, and mental
health history are controlled. While the iden­
tification of a child as having a disability
should set in motion a chain of events resulting
in increased attention and support for the
child, the resulting experiences for the child
may not always be positive. It is imperative
that we continue to monitor the ways in which
school-based classifications are made, inter­
ventions are implemented, and children are
evaluated following the introduction of special
education services.
Finally, it is important that future research­
ers recognize that while adversity in the early
years of development poses significant threats
May 2015 / 193
to the individual's later social and behavioral
health, there are evidence-based interventions
that may help to reduce the likelihood of
problem behavior for children and youth
who are otherwise at risk (Brody, Breach,
Philibert, Chan, & Murry, 2009). These
interventions generally involve a multilevel,
systemic approach to prevention, including
individual-level, family-level, and schoollevel components (Farmer & Farmer, 2001).
Future research studies should continue to
address the efficacy of such preventative
programs, beginning always with the assump­
tion that all children and youth—regardless of
race or background— have the same needs for
security, support from others, and belief in their
own competence and worth.
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AUTHORS' NOTE
Address correspondence to David E. Barrett,
Eugene T. Moore School of Education, Clemson University, 101 Tillman Hall, Clemson, SC
29634; E-mail: bdavid@clemson.edu.
MANUSCRIPT
Initial Acceptance: 12/14/2014
Final Acceptance: 4/03/2015
May 2 0 1 5 /1 9 5
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J Abnorm Child Psychol (2013) 41:641–652
DOI 10.1007/s10802-012-9695-7
Sex and Age Differences in the Risk Threshold
for Delinquency
Thessa M. L. Wong & Rolf Loeber &
Anne-Marie Slotboom & Catrien C. J. H. Bijleveld &
Alison E. Hipwell & Stephanie D. Stepp & Hans M. Koot
Published online: 25 November 2012
# Springer Science+Business Media New York 2012
Abstract This study examines sex differences in the risk
threshold for adolescent delinquency. Analyses were based
on longitudinal data from the Pittsburgh Youth Study (n0503)
and the Pittsburgh Girls Study (n0856). The study identified
risk factors, promotive factors, and accumulated levels of risks
as predictors of delinquency and nondelinquency, respectively. The risk thresholds for boys and girls were established at
two developmental stages (late childhood: ages 10–12 years,
and adolescence: ages 13–16 years) and compared between
boys and girls. Sex similarities as well as differences existed in
T. M. L. Wong (*) : A.-M. Slotboom : C. C. J. H. Bijleveld
Faculty of Law, Department of Criminal Law and Criminology,
VU University Amsterdam,
De Boelelaan 1105,
1081 HV, Amsterdam, The Netherlands
e-mail: thessawong@gmail.com
A.-M. Slotboom
e-mail: m.slotboom@vu.nl
C. C. J. H. Bijleveld
e-mail: Cbijleveld@nscr.nl
R. Loeber : A. E. Hipwell : S. D. Stepp
School of Medicine, Department of Psychiatry,
University of Pittsburgh,
201 N. Craig St., 408 Sterling Plaza,
Pittsburgh, PA 15213, USA
R. Loeber
e-mail: loeberr@upmc.edu
A. E. Hipwell
e-mail: hipwae@upmc.edu
S. D. Stepp
e-mail: steppsd@upmc.edu
H. M. Koot
Department of Developmental Psychology,
VU University Amsterdam,
Van der Boechorststraat 1,
1081 BT, Amsterdam, The Netherlands
e-mail: j.m.koot@vu.nl
risk and promotive factors for delinquency. ROC analyses
revealed only small sex differences in delinquency thresholds,
that varied by age. Accumulative risk level had a linear
relationship with boys’ delinquency and a quadratic relationship with girls’ delinquency, indicating stronger effects for
girls at higher levels of risk.
Keywords Self-reported delinquency . Sex differences .
Threshold hypothesis . Risk and promotive factors . Area
under the curve
Introduction
Many girls involved in the juvenile justice system—those
who are arrested, adjudicated or incarcerated—have been
exposed to trauma or abuse, have mental health as well as
academic problems, and come from multi-problem families
(Chamberlain and Moore 2002; Kataoka et al. 2001;
Lederman et al. 2004; Slotboom et al. 2011). Compared to
arrested, adjudicated, or incarcerated boys, girls in the juvenile justice system have more problems and are exposed
more to known risk factors (Belknap and Holsinger 2006;
Emeka and Sorensen 2009; Gavazzi et al. 2006; Gover
2004; Johansson and Kempf-Leonard 2009). This has been
interpreted as delinquent girls having a more problematic
background than delinquent boys, which has also been
rephrased as the ‘threshold’ hypothesis, i.e. that girls pass
a higher critical ‘risk level’ in order to become delinquent.
This hypothesis was initially defined for antisocial personality disorder (Cloninger and Gottesman 1987) and later
expanded to other developmental disorders (Eme 1992).
A threshold has been defined as the point that must be
exceeded to begin producing a given effect or result
(www.thefreedictionary.com). Thresholds are encountered
in many areas of (social) science and generally denote a
642
critical value, under which a certain effect is not present and
above which it is, such as the absolute hearing threshold in
medicine, or the extinction theshold in ecology. In the
manner in which the ‘threshold’-hypothesis has been
phrased in criminology, it denotes the ‘risk level’ above
which the probability to be delinquent is larger than the
probability not to be delinquent.
This ‘risk level’ that defines the risk threshold can, however, be operationalized in two ways. First, it can be operationalized as the severity or level of a single risk factor: having
a problematic relationship with parents is a risk factor for
delinquency, and only those youth with a very problematic
parent–child relationship have a risk level that is high enough
to pass the threshold to offend. The other way of operationalizing risk level is derived from the cumulative risk approach
(Rutter 1979; Sameroff et al. 1987) and defines the risk level
as the number of risk factors. Thus, according to this operationalization the more risk factors someone experiences, the
more likely he or she is to be delinquent. There is evidence for
such a dose–response relationship between the number of risk
factors and the likelihood of delinquency for boys and girls
(Johansson and Kempf-Leonard 2009; Loeber, Slot and
Stouthamer-Loeber 2008; Van der Laan and Van der Schans
2010; Wong et al. submitted).
A key issue that is unresolved in the literature and that is
the focus of this study, is whether there are sex differences in
the risk threshold for delinquency; differences between boys
and girls in such a threshold for delinquency, while often
posited, have hardly been studied empirically.
Sex Difference in Risk Thresholds
Alemagno et al. (2006) examined the number of risk factors
of 250 detained boys and girls and found that incarcerated
girls were exposed to more risk factors than their male
counterparts. Van der Laan and Van der Schans (2010)
showed, using a similar analytical strategy, that arrested
girls were exposed to more risk factors in the family domain
than arrested boys. Although the results of these studies
concurred with the differential risk threshold hypothesis,
they did not show that such a differential threshold exists
for delinquency, since all studies investigated samples of adjudicated or incarcerated juveniles. Given that girls and women
are often treated differently in the juvenile justice system, the
threshold for delinquency cannot be separated from the threshold to be arrested, prosecuted or convicted (e.g., Daly 1994).
Thus it is problematic to attribute sex differences in the number
of risk factors in officially delinquent samples to the threshold
for delinquency. This may also explain seemingly incompatible
findings, such as that arrested boys have in fact a higher number
of risky lifestyle factors compared to arrested girls (Van der Laan
and Van der Schans 2010). Self-reported delinquency studies
tend not to have the confounding effect of justice processing.
J Abnorm Child Psychol (2013) 41:641–652
Wong et al. (submitted) investigated sex differences in
the delinquency threshold using self-reported data of a
Dutch population-based sample, and did not find support
for a sex-related threshold. In contrast to the previously
mentioned studies, the authors included a comparison group
of nondelinquents. The use of such a comparison group is
necessary, as without this group it is impossible to determine
whether delinquent girls have a higher risk level than delinquent boys or vice versa. Furthermore, the authors examined, in addition to risk factors, the extent to which
promotive factors influenced the risk of later delinquency.
Promotive factors are those factors associated with a decreased probability of delinquency (Sameroff et al. 1998;
Stouthamer-Loeber et al. 2002).1 Since promotive factors
can neutralize risks (Stouthamer-Loeber et al. 2002; Van der
Laan and Blom 2006), ignoring these factors might result in
overstating the importance of risk factors and might make it
impossible to assess any accurate threshold effect.
Although the study by Wong et al. (submitted) had fewer
limitations than previous studies, the authors did not investigate the threshold as such as they compared risk levels of
delinquents with those of nondelinquents. The present study
will improve upon previous research firstly by actually
assessing the threshold itself, i.e. identifying the exact cut
off value, for boys and girls. Secondly, this study will
improve on previous studies by investigating whether the
threshold varies with age and/or sex. Boys’ and girls’ involvement in delinquency changes with age, and criminal
careers develop differently for boys and girls (Junger-Tas et
al. 2003; Wong et al. 2012). Girls’ delinquency tends to
peak earlier than that of boys, i.e. at age 15 versus at age 16
(Junger-Tas et al. 2003; Slotboom, et al. 2011). It remains to
be seen whether delinquency thresholds vary with age for
each sex. As Moffitt (1993) suggested, during puberty, it is
almost normative to show some delinquent behavior.
Thirdly, this study will add to previous research by incorporating sex-shared as well as sex-specific risk factors for
delinquency (Wong et al. 2010; Zahn 2009).
We will address the following research questions: 1) Is
the age-crime curve for girls lower than that of boys? 2)
Which shared and sex-specific risk and promotive factors
measured in middle childhood (ages 7 to 9) and late childhood (ages 10 to 12), respectively, predict self-reported
delinquency in late childhood (ages 10 to 12) and adolescence (ages 13 to 16)? 3) Are there sex differences in
1
In the literature a distinction is made between promotive and protective factors. Protective factors refer to factors that moderate the effect
of risk factors on problem behavior. There should be an interaction
effect with risk factors to be denoted a protective factor (see for
example Rutter 1987). In our study we refer to factors that directly
decrease the probability of delinquency, there is no need for interaction
with risk factors. In line with previous literature, we refer to these
factors as promotive factors.
J Abnorm Child Psychol (2013) 41:641–652
exposure to risk and promotive factors? 4) Are there linear
or quadratic differences in the relationship between cumulative risk and promotive factor score and delinquency for
each sex? 5) Are there differences by sex and age in the
optimal cumulative threshold to predict delinquency?
The questions are addressed using data from the Pittsburgh
Youth Study (PYS) and the Pittsburgh Girls Study (PGS)
using self-reported delinquency as outcomes at late childhood
and adolescence. The studies contain a broad array of risk and
promotive factors known to predict delinquency in previous
studies (e.g., Hoeve et al. 2009; Hubbard and Pratt 2002;
Lipsey and Derzon 1998; Maguin and Loeber 1996; Pratt
and Cullen 2005; Simourd and Andrews 1994; Wong et al.
2010; Zahn 2009). These include individual (problem) factors
(i.e., birth problems, early disruptive behavior disorder, callous unemotional behavior, anxiety, early puberty), family
factors (i.e., poor education of parents, single parent household, physical punishment, communication with parents, positive parenting, supervision, parent–child relationship), school
factors (i.e., truancy, school motivation, school achievement),
peer delinquency, and neighborhood problems.
Methods
Sample
The PYS is a longitudinal study that started in 1987 (Loeber
et al. 2008), consisting of three samples of boys who were in
grades one, four, and seven, respectively, at the start of the
study. Boys who attended public schools in Pittsburgh participated in the study. In the initial screening assessment,
information about the boys’ antisocial behavior was collected through the boys themselves, the caretakers, and their
teachers. On the basis of this information, a risk score was
calculated and all of the boys with the highest scores on
antisocial behavior (n0c. 250, for every sample) were selected for follow-up, while a random sample of the remaining boys (N0c. 250) were also included in the follow-ups.
Only boys from the youngest sample (n0503) were included in the present study. In the first four years of the followups, interviews were conducted by trained interviewers every half year with the boys and one or both caretakers. In the
same period, one of the boys’ teachers was asked to rate the
boys’ behavior. Subsequently, interviews were held every
year. For the current analyses, information about grades was
transformed in age-specific data.
The PGS is also a longitudinal study, but is based on a
stratified, random sample from all households in Pittsburgh
with a girl between the age of 5 and 8 (Keenan et al. 2010).
Disadvantaged neighborhoods were oversampled. The final
sample consists of 2,451 families. To make the samples of
PGS and PYS youth comparable, the current study included
643
only girls aged 7 or 8 at the initial assessment, who attended
public schools at the first assessments in 2000 (n0856).
Follow-ups in the PGS consisted of yearly interviews with
the girls, their caretaker and teacher ratings.
Measurements
To achieve comparability between the sexes, only measurements were included that were comparable across the PYS
and the PGS.
Delinquency Delinquency was measured at ages 11–16
through the 40-item Self-Reported Delinquency Scale
(SRD; Loeber et al. 1998) which was based on an adaptation
of the National Youth Survey (Elliott et al. 1985). For each
of the offenses, respondents indicated whether they had
committed a delinquent act, and if so, how often in the
previous year. For this study we focused on moderate to
serious delinquency (see details in Loeber et al. 1998),
which included breaking-and-entering, stealing things worth
more than 5 dollars, purse snatching, stealing from a car,
dealing in stolen goods, joyriding, vehicle theft, attacking
with intent to injure, forcible robbery, and gang fighting. All
offences were summed and dichotomized into 0 (no offence
committed—nondelinquent) and 1 (1 or more offences committed—delinquent). At age 11 the dealing in stolen goods
item was accidentally not assessed in the PGS, so we did not
include this item in the delinquency construct for both boys
and girls.
The SRD was judged to be too difficult to understand for
the youngest respondents. For that reason, the Self-Reported
Antisocial Behavior Scale (SRA) instead of the SRD was
administered at age 10. Since boys were selected in the first
wave by grade and therefore had different ages, and since
the switch from SRA to SRD was made in one phase for all
boys, some of the 10-year-old boys filled out the SRA en
some the SRD. For girls, the switch was made after the age
of 10 and therefore all 10-year-old girls reported on the
SRA. The SRA consisted of 27 items of delinquent behavior
that were appropriate to younger children (Loeber et al.
1998). For the current study, only those items from the
SRA that were comparable to the selected SRD items were
used to construct the delinquency scale: theft from building,
theft from a car, and purse snatching. After the creation of
the moderate and serious delinquency constructs for each
age, we prepared summary constructs for age blocks in late
childhood (ages 10 to 12) and adolescence (ages 13 to 16),
contrasting nondelinquents with delinquents (1 or more
offences committed at this age).
Risk and Promotive Factors Table 1 lists all constructs used
in this study based on comparable measures in the PYS and
PGS. For most factors, we created two age blocks: for late
Scared
CBCL
Highest degree
of education
Poor education
of parents
Early pubertal
development
Low school
motivation
Low school
achievement
Bad quality
relationship
with primary
caretaker
Truancy
SRD
SRD
Works not hard
compared to peers
CBCL & TRF
Child
Parent–child
Relationship
Survey (PCRS)
Parent–child
Relationship
Survey (PCRS)
Works not hard
compared to peers
CBCL & TRF
Child
SIS
SIS
Child
Parent and
teacher
Teacher
7–9 (n01223);
10–12 (n01225)
7–9 (n01212);
10–12 (n01188)
11–12 (n01273)
7–9 (n01306);
10–12 (n01282)
7–9 (n 01320);
10–12 (n01282)
7–9 (n01308);
10–12 (n01283)
Parent Practices
Scale (PPS)
Parent Practices
Scale (PPS)
Child
7–9 (n01291);
10–12 (n01274)
Child
Supervision and
Involvement
Scale (SIS)
Supervision and
Involvement
Scale (SIS)
Low
communication
about activities
with both
parents
Low positive
parenting
of both
parents
Low supervision
7–9 (n01304);
10–12 (n01284)
Child
Parent–child
Conflict Tactics
Scale (CTSPC)
Discipline
Physical
punishment
of both parents
8–9 (n01348);
10–12 (n01282)
9 (n01126);
12 (n01258)
7–9 (n01324);
10–12 (n01285)
7–9 (n01297);
10–12 (n01281)
7–9 (n01327);
10–12 (n01310)
First assessment
(n01359)
Parent
Child
Petersen Pubertal
Development
Scale (PPDS)
How many
caretakers?
Petersen Pubertal
Development
Scale (PPDS)
How many
caretakers?
Single parent
household
Parent
Parent
Parent
Parent
Highest degree
of education
Child Symptom
Inventory (CSI)
Psychopathy
Screening Device
Early disruptive
behavior
disordera
First assessment
(n01177)
9 items
(alpha from 0.64 to 0.71)
1 item
1 item
4 items
(alpha from 0.54 to 70)
16 items
(alpha from 0.83 to 0.91)
14 items
(alpha from 0.71 to 0.97)
10 items
(alpha from 0.64 to 0.84)
1 item
1 item
5 items
(alpha from 0.56 to 0.75)
7 items
(alpha from 0.54 to 0.61)
1 item
32 items
(alpha from 0.90 to 0.93)
ADHD: 27 items;
ODD: 18 items;
CD: 18 items
15 items
Parent
Reliability
Pre and Perinatal
Risk Factors
Ages
Boys
Assessed by
Girls
Callous
unemotional
behaviorb
Anxiety
Boys
Instruments
Birth and
developmental
history
Diagnostic
Interview
Schedule for
Children (DISC)
Child Behavioral
Checklist (CBCL)
Birth problems
Constructs
Table 1 Constructs used in this study
9 items
(alpha from 0.88 to 0.97)
1 item
1 item
4 items
(alpha from 0.45 to 0.61)
16 items
(alpha from 0.86 to 97)
14 items
(alpha from 0.71 to 0.97)
10 items
(alpha from 0.52 to 0.87)
1 item
1 item
5 items
(alpha from 0.50 to 0.69)
29 items
(alpha from 0.90 to 0.92)
1 item
6 items
(alpha from 0.56 to 0.69)
ADHD: 14 items;
ODD: 8 items;
CD: 12 items
7 items
Girls
Highest 25 %
Highest 25 %
Truant at both ages
Highest 25 %
Highest 25 %
Highest 25 %
Highest 25 %
Highest 25 %
Living with one
parent at
all ages
No diploma or a
General Education
Diploma (GED)
for both parents
at all ages
Highest 25 %
Highest 25 %
Any pre- or
perinatal
birth problem
At least one
of the following
disorders: ADHD,
ODD, CD
Highest 25 %
Risk
Lowest 25 %
Lowest 25 %
NA
Lowest 25 %
Lowest 25 %
Lowest 25 %
Lowest 25 %
No physical
punishment
at all ages
Living with
both parents
at all ages
Lowest 25 %
NA
Lowest 25 %
Lowest 25 %
NA
NA
Promotive
644
J Abnorm Child Psychol (2013) 41:641–652
c
For 7-to-9-year-olds, exactly the same offences were included (vandalism, shoplifting, stealing at school, stealing from building, violence against adult) in the PYS and the PGS. For 10-to-12-yearolds, the peer delinquency scale was similar in the PGS, but included more serious offences in the PYS. Therefore, we only took those offences of the PYS into account that were comparable to those
of the PGS (and which are also similar to the offences considered at earlier ages), i.e. vandalism, stolen something up to $100, stealing from building, and hitting someone with intent to hurt. We
corrected for the number of possible items.
645
b
In the PYS a construct is created that measures psychopathic features in childhood, assessed by the CBCL. Examples of items are ‘lying or cheating’ ‘sudden changes in mood or feelings’, and
‘behaving irresponsibly’. In the PGS, items from the PSD were used to create a similar construct for girls. The following items are included: concerned about school or tasks, keeps promises, feels
bad about doing wrong, concerned about others’ feelings, shows feelings and emotions, keeps the same friends.
Due to the time of the assessment, the diagnoses of ADHD, ODD, and CD in the PYS were based on the DSM-III-R, whereas the diagnoses in the PGS were based on the DSM-IV. To make
diagnoses comparable, we only included those symptoms that were assessed in both studies. For ADHD, the age of onset, that is usually part of the diagnosis, could not be taken into account since it
was not assessed in the PGS. To reach the diagnosis of ADHD, boys and girls had to have 9 symptoms or more. For the diagnosis of CD, 3 or more symptoms were required, and for the diagnosis of
ODD, 4 or more symptoms.
a
Lowest 25 %
Highest 25 %
7–9 (n01312)
10–12 (n01282)
Your Neighborhood
Neighborhood
problems
Your Neighborhood
Parent
17 items
(alpha from 0.93 to 0.96)
7–9: 5 items
(alpha from 0.78 to 0.80);
10–12: 6 items
(alpha from 0.75 to 0.78)
17 items (alpha from 0.94 to 0.96)
5 items
(alpha from 0.68 to 0.84)
7–9 (n01271);
10–12 (n01248)
Peer Delinquency
Scale (PDS)
Peer delinquencyc
Peer Delinquency
Scale (PDS)
Child
Boys
Boys
Instruments
Constructs
Table 1 (continued)
Girls
Ages
Assessed by
Reliability
Girls
Risk
Highest 25 %
Promotive
Lowest 25 %
J Abnorm Child Psychol (2013) 41:641–652
childhood and adolescence. Birth problems and early disruptive behavior disorder were only assessed in the first
assessment and regarding early pubertal development only
the measurements prior to the delinquency age blocks were
included (i.e. age 9 and age 12). In the PGS, no information
about single parent households was available at the age of 7,
so the late childhood age block regarding single parent
households only contained age 8 and 9. Truancy was only
measured at age 11 and 12, so the late childhood age block
was not created.
In creating the constructs from reported waves, missing
constructs were coded as missing if more than 33 % was
missing. If fewer were missing, the mean of the available
responses was substituted for the missing data. In creating
the age blocks, only the non-missing ages were used to
calculate the age blocks for a respondent. The age block
was set to be missing if the construct was missing at all ages.
To identify the risk versus promotive effect of the factors
we used the same method as Stouthamer-Loeber et al.
(1993). All age blocks were trichotimized into a promotive,
a neutral and a risk component using the sex-specific 25th
and 75th percentiles of the age block distributions as cutoffs. The age blocks were recoded into two variables: a risk
variable and a promotive variable. The reference category in
each variable was the neutral component (the 26th to the 74th
percentile of the distribution). The exceptions were birth
problems, early disruptive behavior disorder, poor education
of the parents, and child’s truancy, because these were
inherently dichotomous. Another exception was the age
block for single parent households. In this case, it was more
appropriate to trichotimize according to the number of years
the household consisted of a single parent (i.e. risk: single
parent in all years of age block; promotive: both parents in
all years of age block; neutral see Table 1).
Analyses
First, we established which risk and promotive factors predicted delinquency at late childhood and adolescence, respectively. These analyses were carried out separately for
boys and girls and separately for the two age periods. If a
factor predicted delinquency (p<0.05), this was regarded as
a risk effect; if a factor predicted low or nondelinquency,
this was regarded a promotive effect. If both variables were
related to delinquency, this was regarded both a combined
risk and a promotive effect. Some risk factors predicted
delinquency in boys and girls and were labeled shared risk
factors. The same applied to factors predicting nondelinquency in boys and girls and were labeled shared promotive
factors. Factors that were only related to delinquency in
either boys or girls were labeled sex-specific risk and promotive factors. Odds Ratios were calculated for the risk and
646
promotive factors: an Odds Ratio larger than 1 with a pvalue<0.05 indicates that the presence of the risk factor
significantly increased the prediction of delinquency, while
an Odds Ratio smaller than 1 with a p<0.05 indicates a
promotive factor that significantly predicted nondelinquency.2
Next, we created three types of cumulative risk level
indexes. The first index consisted of the number of significant risk factors in the data set. A second index indicated the
number of significant promotive factors in the data set. The
third, called the combined risk index indicated the number
of significant risk factors minus the number of significant
promotive factors. Because the three risk indexes were
created by taking into account shared factors as well as
sex-specific factors, each risk index consisted of slightly
different risk and promotive components for boys and girls.
Thresholds were studied at two levels. First, we studied
whether the distribution of the relationships between cumulative risk were similar for boys and girls; for this we carried
out a curve fitting analysis to see whether cumulative risk
indexes predicted delinquency in a linear or quadratic way
for boys and girls. If, for example, a quadratic function
applied to one but not the other sex, this indicated that the
risk of future delinquency accelerated faster for one sex
compared to the other.
In a second set of analyses, we examined whether a
threshold could be empirically established by means of
signal detection theory (Swets 1964). Receiver Operating
Curves (ROC) were calculated with Area Under the Curve
(AUC) indicating how well a cumulative risk index predicted delinquency. The analyses also allow the identification of optimal prediction thresholds in which, for every
possible cut-off, the trade-off between the false negative
and false positive rates is calculated. AUC values can range
from 0 (total inaccuracy) to 1 (perfect accuracy). A value of
0.5 indicates that the model is not better than chance, a value
between 0.5 and 0.75 is regarded as fair, between 0.75 and
0.92 as good, between 0.92 and 0.97 as very good and
between 0.97 and 1 as excellent (McFall and Treat 1999).
The Youden’s index, a function of sensitivity (number of
true positives) and specificity (number of true negatives),
was used to identify the optimal cut-off point (Youden
1950). The optimal cut-off is the value with the highest
combination of sensitivity and specificity. This cut-off point
is the threshold for delinquency. We carried out these analyses separately for late childhood and adolescence and for
boys and girls. List wise deletion was used to deal with the
missing information in the analyses.
2
The large number of tests is done to create a subset of variables on
which to run a comprehensive analysis, to filter out those that are not
relevant. Subsequently, boys and girls are compared. So, while this
increases the risk for type I errors because of the multiple testing, this
occurs for boys as well as girls. For that reason, the comparison is still
valid.
J Abnorm Child Psychol (2013) 41:641–652
Results
Table 2 shows the descriptive results. The average number
of measured risk and promotive factors are presented for
boys and girls in middle and late childhood as well as the
number of delinquents in late childhood and adolescence.
No sex differences were found regarding the average number of measured risk and promotive factors. The prevalence
of delinquency differed by gender in both late childhood as
well as in adolescence.
The first question we addressed was: Is the age-crime
curve for girls lower than that of boys? Figure 1 shows that
at age 10 there was only a small, although significant (3.6 %
vs. 1.8 %; p<0.05) sex difference in the prevalence of
moderate to serious delinquency, but at all other older ages
the prevalence of delinquency was higher for boys than girls
(for all ages p<0.01). However, the peak age of the agecrime was the same for the two sexes (age 14).
The second question that we posed was: Which shared
and sex-specific risk and promotive factors measured in
middle childhood (ages 7 to 9) and late childhood (ages 10
to 12), respectively, predict self-reported delinquency in late
childhood (ages 10 to 12) and adolescence (ages 13 to 16)?
Table 3 shows the odds ratios of the risk and promotive
factors for boys and girls in the two age periods. An empty
cell indicates that there is no statistically significant risk (or
promotive) effect of a given factor.
The results showed that delinquent behavior of boys and
girls is related to many different factors. As Table 3 shows,
many risk and promotive factors are shared by boys and girls,
but some differences were found between boys and girls, and
between age periods as well. Risk and promotive factors that
were shared were callous-unemotional behaviour, supervision
by parents, relationship with parents, and almost all risk and
promotive factors in the school and peer domain. Differences
between boys and girls were found in the individual domain
regarding birth problems, early disruptive behaviour and anxiety. Birth problems appeared to be a risk factor for delinquency in late childhood for girls and not for boys. Furthermore,
early disruptive behaviour was a risk for delinquency at both
age periods for girls, but not for boys. Also, high anxiety had a
promotive effect on boys in their late childhood, but not on
adolescent boys, while it had an age-invariant effect on adolescent girls. Besides, low anxiety was a risk factor for adolescent girls. Other interesting differences were found in the
family domain. Living with both parents had a promotive
effect on boys’ delinquency in both age periods. For girls,
however, it was only promotive for delinquency in adolescence. Furthermore, not being exposed to physical punishment
was a promotive factor for girls in both age periods, but not for
boys. By contrast, for boys, physical punishment was a risk
factor regarding delinquency in adolescence. Another remarkable difference is that communication about activities with
J Abnorm Child Psychol (2013) 41:641–652
647
Table 2 Descriptive results
Middle childhood
Average number of risk factors (n01316)
Average number of promotive factors (n01282)
Late childhood
Average number of risk factors (n01318)
Average number of promotive factors* (n01281)
% delinquent*
Adolescence
% delinquent*
Boys (n0503)
Girls (n0856)
Average
3.43 (2.33)
2.92 (2.11)
3.29 (2.28)
3.00 (2.37)
3.34 (2.30)
2.97 (2.28)
3.41 (2.24)
2.95 (2.17)
24.5 %
3.19 (2.34)
3.42 (2.51)
9.7 %
3.27 (2.31)
3.24 (2.40)
15.2 %
42.6 %
21.2 %
29.2 %
Standard deviations are in parentheses. With t-tests it was tested whether boys and girls differed in number of risk and promotive factors. Crosstabs
were used to test the difference in delinquency prevalence
*significantly different for boys and girls at p<0.05
parents only affected delinquency for girls and only during
puberty, both as a risk and a promotive factor. Positive parenting was also only related to girls’ delinquency. More specifically, lack of positive parenting was a risk for girls in both age
periods and a promotive factor for delinquency in adolescence.
Next we asked: Are there sex differences in exposure to
risk and promotive factors? Table 4 shows the average
number of (significant) risk factors and (significant) risk
minus promotive factors for nondelinquent and delinquent
boys and girls during middle and late childhood. Delinquent
boys and girls averaged higher risk scores than nondelinquent boys and girls, respectively. Furthermore, delinquent
girls averaged a higher number of risk factors than delinquent boys at each age period. When averages of risk and
promotive factors were considered, delinquent girls compared to delinquent boys scored higher at middle childhood
only. At late childhood, average exposure to risk and promotive factors was similar for of delinquent boys and girls.
The fourth question we asked was: Are there linear or
quadratic differences in the relationship between cumulative
risk and promotive factor score and delinquency for each sex?
Curve fitting analyses showed that for both age periods
positive linear relationships between the risk levels and delinquency were found for boys (with R2 of 0.07 and 0.15 respectively; other relationships had a worse fit to the data), but
% delinquents
25%
20%
15%
% delinquent boys
10%
% delinquent girls
5%
0%
positive quadratic relationships for girls (with R2 of 0.06 and
0.17 respectively, again other relationships had a worse fit to
the data; see the modeled relationships in Figs. 2 and 3). This
indicates that, regardless of sex, the more risk factors boys and
girls were exposed to, the more likely they were to be delinquent. However, for boys the increase in likelihood for delinquency was similar across risk levels, whereas for girls the
increase in likelihood was amplified at every next risk level.
More specifically, because of the linear relationship for boys,
every increase in the number of risk factors was associated
with 5.2 % more delinquent boys in late childhood and 7.3 %
more delinquent boys in adolescence. For girls, because of the
quadratic relationship, this increase depended on the risk level.
An increase in the risk level from 3 to 2 promotive factors (in
middle and late childhood respectively), for instance, was
associated with 0.6 % more delinquent girls in late childhood
and to 3.3 % more in adolescence, whereas an increase in the
risk level from 3 to 4 or more risk factors (in middle and late
childhood) was associated with 5.4 % and 10.5 % more delinquent girls in late childhood and adolescence, respectively.
Thus, for girls we see that the effect of a one-step risk increase
becomes ever stronger: the higher the risk level, the larger the
corresponding shift in delinquency at an increase in risk.
The final question concerned: Are there differences by
sex and age in the optimal cumulative threshold to predict
delinquency? The results regarding the predictive power of
the combined risk levels on late childhood delinquency for
boys and girls are in Fig. 2: girls had slightly higher AUC
values than boys (0.74 vs. 0.68). Furthermore, the optimal
cut-off point for girls was higher than for boys (1 vs. 0 risk
factors) 3 which indicates that girls have a higher threshold
for delinquency in late childhood than boys.
10 11 12 13 14 15 16
Age
Fig. 1 Age crime curve for moderate to serious delinquency by sex
3
Sensitivity and specificity at the selected threshold for late childhood
delinquency were respectively 0.57 and 0.69 for boys and respectively
0.74 and 0.63 for girls.
648
J Abnorm Child Psychol (2013) 41:641–652
Table 3 Odds ratios of risk and promotive factors for delinquency at ages 10 to 12 and ages 13 to 16, by sex
Factors
Delinquency (10 to 12 years)
Risk
Boys
Birth problems
Disruptive behavior
Callous unemotional
behavior
Anxiety
Poor education of
parents
Early pubertal
development
Single parent household
Physical punishment of
parents
Communication with
parents
Positive parenting
Supervision
Relationship with
primary caretaker
Truancy
School motivation
School achievement
Peer delinquency
Neighborhood problems
Delinquency (13 to 16 years)
Promotive
Girls
Risk
Boys
Girls
Boys
0.35**
0.18**
2.15**
1.82**
3.22**
2.39**
3.21**
Promotive
Girls
3.16**
0.37**
1.88*
1.83*
2.80**
2.32**
2.20**
2.57**
1.97**
0.41**
0.35**
2.28**
3.37**
0.64*
2.59**
1.93**
2.19**
Girls
2.64**
0.62*
2.20**
Boys
0.41*
0.20**
0.35**
0.48*
0.32**
0.52*
0.44*
0.38*
0.12**
0.47**
0.62*
0.53**
0.64*
2.08**
0.34**
1.63*
2.31**
2.86**
0.41**
0.44**
0.55*
0.35**
0.29**
6.10**
2.13**
1.93**
4.96**
1.79**
0.46**
0.50**
0.23**
0.54**
0.28**
0.42**
0.18**
0.58*
1.82*
2.44**
2.83**
4.18**
2.29**
3.78**
1.60*
*p<0.05, ** p<0.01
Next, adolescent delinquency was predicted from risk levels at the age of 10 to 12 (see Fig. 3). Girls had slightly higher
AUC values (0.77 vs. 0.72), but boys had a higher optimal
cut-off point than girls (1 vs. 0 risk factors).4 Boys therefore
have a higher threshold than girls to become delinquent in
adolescence. Thus, we see that there are no consistent differences in the delinquency threshold for boys and girls: the
thresholds differ by age period. The differences are also small;
however, as the threshold is a group-value and not the average
of a set of individual-level values, we cannot test whether it
differs significantly for boys and girls.
Discussion
This study examined whether boys and girls had different risk
thresholds for delinquency at two age periods (late childhood
and adolescence). Using data from the PYS and PGS studies,
we first tested which factors (at ages 7 to 9 and 10 to 12) had a
risk effect, a promotive effect, or both. Boys and girls
4
Sensitivity and specificity at the selected threshold for adolescent
delinquency were respectively 0.47 and 0.85 for boys and respectively
0.67 and 0.74 for girls.
appeared to share many risk and promotive factors, but sex
differences and differences between age periods were found as
well. This indicates that delinquent girls might need different
types of interventions than delinquent boys, and that the age of
the delinquent should be taken into account.
Not surprisingly, boys and girls who were delinquent
appeared to have higher risk levels than their nondelinquent
counterparts. Within the delinquents, girls on average had
higher number of risk factors than boys when only risk factors
were considered. When promotive factors were taken into
account as well, girls compared to boys had on average a
higher risk levels in middle childhood. In late childhood, the
risk level of delinquent boys and girls was similar.
The relationship between the risk level and delinquency
was linear for boys, indicating that every extra risk factor
resulted in a similar step-wise increase regarding delinquency
probability. For girls, however, this relationship turned out to
be non-linear, with the increase in the probability of delinquency larger at the higher risk level ranges than in the lower part.
Thus, at low risk levels, an additional risk factor gives but a
small increase in the delinquency probability. However, at
higher risk levels, one extra risk factor augments this probability substantially for girls. Due to this amplification,
J Abnorm Child Psychol (2013) 41:641–652
649
Table 4 Means and standard deviations of risk levels for nondelinquent and delinquent boys and girls
Middle childhood
Average number of
risk factors
Average number
of risk minus
promotive factors
Late childhood
Average number
of risk factors
Average number
of risk minus
promotive factors
Boys (n0503)
Girls (n0856)
Sex difference between
delinquents
Nondelinquent Delinquent Difference
within boys
Nondelinquent Delinquent Difference
within girls
1.15 (1.27)
1.95 (1.41) t (468)05.66**
2.19 (1.76)
3.82 (1.81) t (804)07.85**
t (192)08.11**
−0.89 (2.41)
0.65 (2.40) t (468)04.96**
0.61 (2.61)
2.91 (2.11) t (804)07.58**
t (192)06.77**
1.13 (1.25)
2.32 (1.62) t (444)08.83**
1.63 (1.50)
−1.39 (2.72)
0.92 (2.69) t (444)08.91**
−1.68 (3.18)
3.39 (1.89) t (747)0
t (347)05.70**
12.39**
1.43 (2.61) t (747)011.35** t (347)01.80
Standard deviations in parentheses. Means of nondelinquent and delinquent boys and of nondelinquent and delinquent girls are compared with Ttests as well as those of delinquent boys and girls
*p<0.05, ** p<0.01
delinquent girls would—even with a same delinquency threshold—have higher average risk levels than boys. Therefore,
previous studies that focused on the average risk level for boys
and girls found higher risk levels among delinquent girls than
among delinquent boys (Alemagno et al. 2006; Van der Laan
and Van der Schans 2010).
While higher risk levels are associated with a stronger increase in likelihood of delinquency in girls than in
boys, this study implies that girls do not have a higher
threshold for delinquency. Differences in the threshold
are not apparent and fluctuate with age which might
suggests that no actual sex difference in the threshold
for delinquency exists. All in all, in this study—that
was appropriately designed with a control group, and
sex-specific risk as well as promotive factors—no evidence for a sex-specific delinquency threshold emerged.
The threshold hypothesis was examined using two
complementary approaches: curve fitting and ROC analyses. The curve estimation analyses showed a linear
association between risk level and delinquency for boys
and a curvilinear relationship for girls. The ROC analyses examined the location of the threshold and did not
show sex differences. While there appears to be no
different threshold as such, increases of the risk level
beyond this threshold impact differently on girls than on
boys. That is, from the threshold onwards, risks contribute more and more to the delinquency risk for girls
(due to the quadratic relationship), but not for boys (due
to the linear relationship). This indicates that delinquent
girls might have more problematic backgrounds than
their male counterparts. This has also been shown in
previous research regarding characteristics of juveniles
Fig. 2 Combined risk levels (number of risk factors minus number of promotive factors) at the age of 7 to 9 predicting moderate to serious
delinquency at age 10 to 12, for boys and girls
650
J Abnorm Child Psychol (2013) 41:641–652
Fig. 3 Combined risk levels (number of risk factors minus number of promotive factors) at the age of 10 to 12 predicting moderate to serious
delinquency at age 13 to 16, for boys and girls
in the juvenile justice system (Belknap and Holsinger
2006; Emeka and Sorensen 2009). Zahn et al. (2009)
showed that interventions that target multiple risk factors can reduce delinquent behavior in both boys and
girls. However, given the more problematic background
of girls in the juvenile justice system, for them it might
be even more important to address multiple problems
simultaneously. Likely, gender-specific interventions are
necessary for girls. There is no clear evidence yet about
the effectiveness of existing gender-specific interventions (Zahn et al. 2009).
It is noteworthy that the risk level is a (much) better
predictor for delinquency among girls than among boys,
shown by the AUC level as well as the results regarding
sensitivity and specificity. For boys, the threshold detects
57 % of the delinquents in late childhood and only 47 % in
adolescence. For girls, however, these percentages were
74 % and 67 % respectively. This indicates that the risk
level alone is not enough to predict delinquency, especially
for boys.
Differences with Previous Studies
Several explanations can be put forward for the fact that
most previous studies on the threshold had such different
results than the present study. These explanations regard
differences between previous studies and the present study
regarding the sample, the definition of the threshold, and
regarding the operationalization of risk. With regard to
sample differences, previous studies mainly examined adjudicated or incarcerated samples. In these samples the threshold for delinquency is confounded with the threshold for
criminal justice system involvement. The fact that our study
showed that the threshold for delinquency differs minimally
for boys and girls, these studies probably picked up on
arrest, prosecution or incarceration thresholds.
Concerning differences in the definition of the threshold,
previous studies based their conclusions about sex different
thresholds on risk levels of delinquent boys and girls
(Alemagno et al. 2006; Belknap and Holsinger 2006;
Emeka and Sorensen 2009; Johansson and Kempf-Leonard
2009; Van der Laan and Van der Schans 2010), whereas the
current study identified the location of the threshold.
Because delinquent girls had on average higher risk levels
than boys and because delinquency is less prevalent in girls,
previous studies concluded that girls have a higher threshold
for delinquency. However, the (difference in) location of the
threshold was not assessed.
Regarding the operationalization of risk, there are two
main differences between previous studies and the present
study. First, previous studies did not include promotive
factors to measure risk. However, since the number of
promotive factors can buffer the influence of risk factors
only (Stouthamer-Loeber et al. 2002; Van der Laan and
Blom 2006), it is inadequate to examine only risk factors.
To see how the results would differ if we would have
considered risk factors only, the analyses of the present
study were carried out as well for the risk index that only
considered the number of risk factors.5 Just like in previous
studies (Alemagno et al. 2006; Van der Laan, and Van der
Schans 2010), we found a higher threshold for girls when
we focused solely on risk factors, for both age periods.
Slightly better AUC values showed, however, that models
that included both risk factors and promotive factors were
more adequate than models that considered risk factors only.
Not including promotive factors can lead to overestimation
of the risk and therefore of the threshold. This indeed turned
out to be the case for girls.
Second, the present study included shared as well as sexspecific factors while other studies only focused on shared
5
These results are not presented here, but are available from the first
author.
J Abnorm Child Psychol (2013) 41:641–652
factors (see Moffitt et al. 2001; Junger-Tas et al. 2004).
Again, for the sake of comparison, the analyses of the
present study were carried out as well with models that only
considered shared factors.6 Models that considered both
shared factors and sex-specific factors resulted in a better
prediction of delinquency at puberty for girls than analyses
based on shared factors only. In these latter models, that
were utilized in previous studies, girls’ risks are underestimated and their risk threshold cannot be examined properly.
Our study showed that girls and boys do not differ to a
large extent in their delinquency ‘threshold’, i.e. the risk
level beyond which the probability to be delinquent is
greater than the probability to be not delinquent. It is likely
that the threshold that was picked up in previous studies
among criminal justice samples may actually have been a
criminal justice-involvement threshold. Difference in the
average risk levels of delinquent boys and girls are generated by the increasing impact of risk factors on girls beyond
the delinquency threshold.
Strengths and Limitations
This study had several limitations. First, only moderate to
serious delinquency was taken into account. It might be,
however, that although no large sex differences were found
in the threshold for delinquency in general, boys’ and girls’
thresholds do differ to a large extent for violent or serious
delinquency. As Moffitt (1993) claimed, during puberty, delinquent behavior is more normative, which as we argued may
explain the lack of a clear differential threshold. For less
normative behavior, such a threshold may well emerge. This
is difficult to test, however, since serious (violent) delinquent
behavior is a rare phenomenon in juvenile females and therefore such analyses would have suffered from a lack of power.
Another limitation is that not all factors that have an
important risk or promotive effect on delinquency could be
taken into account. This is because two different studies (the
PYS and the PGS) were combined and we were strict in our
decision not to consider factors that were not conistently
measured in both studies. For instance, negative life events
(i.e. crime victimization, abuse, neglect), that have been
shown to be important in predicting delinquency especially
for girls (Wong et al. 2010), could not be included because
of assessment differences.
Furthermore, delinquency in late childhood might be somewhat underrated since some of the 10-year-old boys but all of
the 10-year-old girls filled out the SRA instead. The SRD was
not filled out by these juveniles, and therefore the SRA was the
only option to compare their delinquent behavior. However,
the SRA included fewer delinquency items which might have
led to underestimation of delinquency.
6
See footnote 5.
651
In line with other studies using the Pittsburgh Youth Study
and the Pittsburgh Girls Study we used mean substitution in
case of missing items. Even though mean substitution is in
principle suboptimal, the data preparation was meant to create
dichotomous variables that represented risk versus no risk
(and promotive versus not promotive). These dichotomous
variables were created by trichotimizing variables into a promotive, a neutral and a risk component using the 25th and
75th percentiles of the variables as cut-offs. As the mean of a
variable falls most often not above the 75th percentile or
below the 25th percentile, the imputed value falls mostly
within the neutral category representing neither risk nor promotion. This is therefore a conservative method, but it is also
in line with what one might suppose to be the case when
scores on a risk factor are missing (namely that there is no
marked high or low score). Therefore, imputing the variables
differently might mean a likely small methodological gain at
the cost of being not congruent any more with previous
analyses and descriptive statistics on these data sets.
Despite these limitations, the present study improved on
previous studies by identifying thresholds for delinquency,
and by taking into account promotive factors. Furthermore
this study focused on self-reports of delinquency, and included
shared as well as sex-specific risk and promotive factors, and
examined thresholds longitudinally at two age periods.
Moreover, we showed that some of our design improvements
actually improved predictions compared to previously studies.
Acknowledgments The authors thank Rebecca Stallings and Deena
Battista for their help with the data preparation.
The writing of this paper was supported by grant 2005-JK-FX-0001
from the Office of Juvenile Justice and Delinquency Prevention
(OJJDP), grants MH 056630, 50778 and 73941 from the National
Institute of Mental Health, grant No. 11018 from the National Institute
on Drug Abuse, and a grant from the Department of Health of the
Commonwealth of Pennsylvania.
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Who Are At-Risk Youth?
Caitlyn Meade
Summer 2017
Roadmap
 Who are at-risk youth?
 What are trends in youth violence?
 How does race relate to youth crime?
 How does criminological theory relate to youth crime?
 Note that this is ONLY AN OVERVIEW—you will not get the same depth on these theories as you
will in your theory class.
 What are the effects of justice involvement?
At-Risk Youth
An at-risk youth is a child who is less likely to transition
successfully into adulthood.
Success can include academic success and job
readiness, as well as the ability to be financially
independent.
It also can refer to the ability to become a positive
member of society by avoiding a life of crime.
Risk Factors for being “At Risk”
 Bad home environment
 Abuse/Neglect
 Bad Parenting
 Poverty
 Neighborhood Factors
 Running Away
 System involvement (Foster Care)
 Poor academic performance
 Victimization
Youth Crime Statistics
 The peak year for juvenile Violent Crime Index arrest rates was 1994.
 Between 1980 and 1994, arrest rates for youth ages 15-17 increased an
average of 73%.
 Between 1994 and 2012, violent crime arrest rates declined for all
age groups
 Rates dropped an average of 64% for youth ages 15-17.
 Across all juvenile age groups, age-specific Violent Crime Index
arrest rates in 2012 were at their lowest level since at least 1980.
Youth Crime
Statistics:
Age & arrests
by year
Demographics of Nonindex Offenses
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Female
Under age 15
White
Black
American Indian
Asian/NHPI
Ages We’re Talking About
Legal definition of juveniles
Under the age of 18
Educational definition
Prior to high school graduation
Biological definition
Under age ~25
Disproportionate Minority Contact
“Disproportionate minority contact refers to the
disproportionate number of minority youth who come into
contact with the juvenile justice system.”
Race and Juvenile Delinquency
 Disproportionate arrests
 Black youth are almost 50% more likely than Whites to have been arrested by
age 18
 Disparities in adjudication
 Blacks more likely to be sent to secure confinement
 Blacks more likely to be transferred to adult facilities
 Blacks comprise 62% of the youth prosecuted in the adult criminal
system and are 9 times more likely than White youth to receive an
adult prison sentence
Race and Juvenile Delinquency
 Why are Blacks more likely to come in contact with the law?
 Community Factors
Disadvantaged communities have higher crime rates, which attracts more police
attention
 Policing strategies
Hot spot policing and broken windows policing emphasize areas with high
crime, increasing patrol of those areas.
Why does increase patrol = increase contact between police and minorities?
 Biases in policing
Implicit and explicit biases held by officers lead to beliefs that minorities are
more criminal
Crim Theory and Racial Differences
 Strain Theory
 Blacks and low SES (socioeconomic status) experience different strains
 Adaptation to strain may be reflective of cultural norms
 Commit crime to achieve “American Dream”
 Sell drugs, steal, etc. to purchase goods that increase an individual’s status
 General Strain
 Some people use crime as a way to cope with negative strains
 Minorities are disproportionately exposed to strains related to SES, discrimination, and
racism
 May cope with such strains using crime—especially when they witness others coping by
committing crime
Crim Theory and Racial Differences
 Social Disorganization Theory
 Community factors affect crime
 When a neighborhood exhibits certain characteristics, they are more likely to be
“socially disorganized,” leading to a breakdown of informal social control. This
facilitates crime in the neighborhood.
Elements of Social Disorganization
 Residential Mobility
 Lots of people moving in and out of the neighborhood
 People care less/are less invested in protecting neighbors/maintaining the neighborhood
 Economic status
 Low SES
 The community and its residents have fewer resources to help prevent crime (such as afterschool programs,
neighborhood watch, etc.)
 Lack of Cohesion
 When people don’t know each other, they don’t care about each other as much.
 People are less likely to “call out” the neighborhood kids for doing bad stuff, less likely to call the cops if they see someone
in trouble and so on
 Ethnic Heterogeneity
 Lots of different cultures leads to language and cultural barriers
 People from different cultures may have different expectations and goals for a neighborhood
 Leads to a lack of cohesion
Crim Theory and Racial Differences
 Social Bonding
 Lack of social bonds
 Attachment
 Attached to other people/school/conventional
aspects of society
 More attached= less crime
 Commitment
 Committed to living lawfully
 More commitment=less crime
 Involvement
 Involved in conventional activities
 More involvement=less crime
 Belief
 Believe in laws and norms of societies
 More belief= less crime
 What are some reasons minorities
may be less bonded to society?
 Single parent households = less
attachment to parent
 Less attachment to school =lower
achievement
 Less time and resources to be
involved in conventional activities
 Norms and beliefs of society are
believed to be negative due to
discrimination experienced
Crim Theory and Racial Differences
 Self-Control
 Self-control prevents an individual from committing crime.
 Individual weighs the pros and cons and can hold off on impulsively
committing crime.
 Parenting
 Parents shape an individuals level of self-control through disciplining their child correctly
 Self-control is argued to be stable through the life-course
 In other words, one’s level of self-control does not change after around age 8
Concept Check
Stop and think about how these theories address criminal behavior.
How do these theories apply to minorities?
What do these theories fail to explain about minorities and crime?
Barrett Article
 Findings
 Evidence of effects of early risk factors on recidivism
 Recidivism predicted by
 Gender, poverty, CPS referral, diagnosis for psych disorder, special education, early age of first offense,
prosecuted for first offense
 Black youth recidivism more strongly predicted by
 Special education
 Gender
 Poverty
 White youth recidivism more strongly predicted by
 Psych diagnoses
 Status offending
 Prosecution for first offense
Long-Term Effects of JJ Contact
 Severity of sentencing
 More severe sentences when you have a record
 “On the radar”
 Police know individual as trouble, more likely to arrest again, etc
 Housing, education, employment, government assistance
 Lose the rights to government assistance, housing, and education for some crimes
 Harder to get a job or place to live with a record
 Johnson Article
 Effects of JJ contact on college
 Black males who reported being arrested less likely to go to college
 Even controlling for background characteristics
Daily Review
 Who are at-risk youth?
 What are the demographics of youth involved in the justice system?
 How does criminological theory explain minorities and crime?
 What does disproportionate minority contact mean?
 How does justice involvement affect juveniles going into
adulthood?

Tutor Answer

1. Why Criminology focuses on Males
It is a fact that criminology often focuses on male members of the society. The main
reason for this can be considered to be the fact that men are committing most crimes in the
community. To deal with the offense, most governments focus their efforts and resources on
dealing with the source of the evil. Creating such focus has both positive and negative effects as
one may deal with crime more effectively, and it can also lead to a sense of overlooking other
causes of crime.
Focusing on men also affects the knowledge that we have in general regarding issues to
do with crime and delinquency. In many cases, the collection of data centers on th...